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Journal articles on the topic 'Evolutionary programming (Computer science) Mathematical optimization'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Jong-Hwan Kim and Hyun Myung. "Evolutionary programming techniques for constrained optimization problems." IEEE Transactions on Evolutionary Computation 1, no. 2 (July 1997): 129–40. http://dx.doi.org/10.1109/4235.687880.

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2

Abo-Elnaga, Yousria, and Sarah Nasr. "Modified Evolutionary Algorithm and Chaotic Search for Bilevel Programming Problems." Symmetry 12, no. 5 (May 6, 2020): 767. http://dx.doi.org/10.3390/sym12050767.

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Bi-level programming problem (BLPP) is an optimization problem consists of two interconnected hierarchical optimization problems. Solving BLPP is one of the hardest tasks facing the optimization community. This paper proposes a modified genetic algorithm and a chaotic search to solve BLPP. Firstly, the proposed algorithm solves the upper-level problem using a modified genetic algorithm. The genetic algorithm has modified with a new selection technique. The new selection technique helps the upper-level decision-maker to take an appropriate decision in anticipation of a lower level’s reaction. It distinguishes the proposed algorithm with a very small number of solving the lower-level problem, enhances the algorithm performance and fasts convergence to the solution. Secondly, a local search based on chaos theory has applied around the modified genetic algorithm solution. Chaotic local search enables the algorithm to escape from local solutions and increase convergence to the global solution. The proposed algorithm has evaluated on forty different test problems to show the proposed algorithm effectiveness. The results have analyzed to illustrate the new selection technique effect and the chaotic search effect on the algorithm performance. A comparison between the proposed algorithm results and other state-of-the-art algorithms results has introduced to show the proposed algorithm superiority.

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IWAMATSU, MASAO. "COMPARISON OF PARTICLE SWARM AND EVOLUTIONARY PROGRAMMING AS THE GLOBAL CONFORMATION OPTIMIZER OF CLUSTERS." International Journal of Modern Physics C 16, no. 04 (April 2005): 591–606. http://dx.doi.org/10.1142/s0129183105007340.

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The particle swarm optimization (PSO) algorithm and two variants of the evolutionary programming (EP) are applied to the several function optimization problems and the conformation optimization of atomic clusters to check the performance of these algorithms as a general-purpose optimizer. It was found that the PSO is superior to the EP though the PSO is not equipped with the mechanism of self-adaptation of search strategies of the EP. The PSO cannot find the global minimum for the atomic cluster but can find it for similar multi-modal benchmark functions of the same size. The size of the cluster which can be handled by the PSO and the EP is limited, and is similar to the one amenable to the popular simulated annealing. The result for benchmark functions only serves as an indication of the performance of the algorithm.

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Nurcahyadi, Teddy, and Christian Blum. "Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study." Mathematics 9, no. 4 (February 11, 2021): 361. http://dx.doi.org/10.3390/math9040361.

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Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. This is also the case in other learning-based metaheuristics such as evolutionary algorithms and particle swarm optimization. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this work we present and study an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. Moreover, we compare our proposal to some well-known existing negative learning approaches from the related literature. Our study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases we are able to show that our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

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Regis, Rommel G. "Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization Using Radial Basis Functions." IEEE Transactions on Evolutionary Computation 18, no. 3 (June 2014): 326–47. http://dx.doi.org/10.1109/tevc.2013.2262111.

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Sato, Mayuko, Yoshikazu Fukuyama, Tatsuya Iizaka, and Tetsuro Matsui. "Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration." Algorithms 12, no. 1 (January 7, 2019): 15. http://dx.doi.org/10.3390/a12010015.

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This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.

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Li, Hecheng, and Lei Fang. "Co-evolutionary algorithm: An efficient approach for bilevel programming problems." Engineering Optimization 46, no. 3 (April 29, 2013): 361–76. http://dx.doi.org/10.1080/0305215x.2013.772601.

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8

Chen, Yi, Aimin Zhou, and Swagatam Das. "Utilizing dependence among variables in evolutionary algorithms for mixed-integer programming: A case study on multi-objective constrained portfolio optimization." Swarm and Evolutionary Computation 66 (October 2021): 100928. http://dx.doi.org/10.1016/j.swevo.2021.100928.

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9

Sutton, Andrew M., Frank Neumann, and Samadhi Nallaperuma. "Parameterized Runtime Analyses of Evolutionary Algorithms for the Planar Euclidean Traveling Salesperson Problem." Evolutionary Computation 22, no. 4 (December 2014): 595–628. http://dx.doi.org/10.1162/evco_a_00119.

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Parameterized runtime analysis seeks to understand the influence of problem structure on algorithmic runtime. In this paper, we contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). We investigate the structural properties in TSP instances that influence the optimization process of evolutionary algorithms and use this information to bound their runtime. We analyze the runtime in dependence of the number of inner points k. In the first part of the paper, we study a [Formula: see text] EA in a strictly black box setting and show that it can solve the Euclidean TSP in expected time [Formula: see text] where A is a function of the minimum angle [Formula: see text] between any three points. Based on insights provided by the analysis, we improve this upper bound by introducing a mixed mutation strategy that incorporates both 2-opt moves and permutation jumps. This strategy improves the upper bound to [Formula: see text]. In the second part of the paper, we use the information gained in the analysis to incorporate domain knowledge to design two fixed-parameter tractable (FPT) evolutionary algorithms for the planar Euclidean TSP. We first develop a [Formula: see text] EA based on an analysis by M. Theile, 2009, ”Exact solutions to the traveling salesperson problem by a population-based evolutionary algorithm,” Lecture notes in computer science, Vol. 5482 (pp. 145–155), that solves the TSP with k inner points in [Formula: see text] generations with probability [Formula: see text]. We then design a [Formula: see text] EA that incorporates a dynamic programming step into the fitness evaluation. We prove that a variant of this evolutionary algorithm using 2-opt mutation solves the problem after [Formula: see text] steps in expectation with a cost of [Formula: see text] for each fitness evaluation.

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10

Urselmann, Maren, Michael T. M. Emmerich, Jochen Till, Guido Sand, and Sebastian Engell. "Design of problem-specific evolutionary algorithm/mixed-integer programming hybrids: two-stage stochastic integer programming applied to chemical batch scheduling." Engineering Optimization 39, no. 5 (July 2007): 529–49. http://dx.doi.org/10.1080/03052150701364659.

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11

Benítez-Hidalgo, Antonio, Antonio J. Nebro, and José F. Aldana-Montes. "Sequoya: multiobjective multiple sequence alignment in Python." Bioinformatics 36, no. 12 (April 21, 2020): 3892–93. http://dx.doi.org/10.1093/bioinformatics/btaa257.

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Abstract Motivation Multiple sequence alignment (MSA) consists of finding the optimal alignment of three or more biological sequences to identify highly conserved regions that may be the result of similarities and relationships between the sequences. MSA is an optimization problem with NP-hard complexity (non-deterministic polynomial-time hardness), because the time needed to find optimal alignments raises exponentially along with the number of sequences and their length. Furthermore, the problem becomes multiobjective when more than one score is considered to assess the quality of an alignment, such as maximizing the percentage of totally conserved columns and minimizing the number of gaps. Our motivation is to provide a Python tool for solving MSA problems using evolutionary algorithms, a nonexact stochastic optimization approach that has proven to be effective to solve multiobjective problems. Results The software tool we have developed, called Sequoya, is written in the Python programming language, which offers a broad set of libraries for data analysis, visualization and parallelism. Thus, Sequoya offers a graphical tool to visualize the progress of the optimization in real time, the ability to guide the search toward a preferred region in run-time, parallel support to distribute the computation among nodes in a distributed computing system, and a graphical component to assist in the analysis of the solutions found at the end of the optimization. Availability and implementation Sequoya can be freely obtained from the Python Package Index (pip) or, alternatively, it can be downloaded from Github at https://github.com/benhid/Sequoya. Supplementary information Supplementary data are available at Bioinformatics online.

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12

GEBSER, MARTIN, ROLAND KAMINSKI, BENJAMIN KAUFMANN, and TORSTEN SCHAUB. "Multi-shot ASP solving with clingo." Theory and Practice of Logic Programming 19, no. 1 (July 10, 2018): 27–82. http://dx.doi.org/10.1017/s1471068418000054.

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AbstractWe introduce a new flexible paradigm of grounding and solving in Answer Set Programming (ASP), which we refer to as multi-shot ASP solving, and present its implementation in the ASP systemclingo. Multi-shot ASP solving features grounding and solving processes that deal with continuously changing logic programs. In doing so, they remain operative and accommodate changes in a seamless way. For instance, such processes allow for advanced forms of search, as in optimization or theory solving, or interaction with an environment, as in robotics or query answering. Common to them is that the problem specification evolves during the reasoning process, either because data or constraints are added, deleted, or replaced. This evolutionary aspect adds another dimension to ASP since it brings about state changing operations. We address this issue by providing an operational semantics that characterizes grounding and solving processes in multi-shot ASP solving. This characterization provides a semantic account of grounder and solver states along with the operations manipulating them. The operative nature of multi-shot solving avoids redundancies in relaunching grounder and solver programs and benefits from the solver's learning capacities.clingoaccomplishes this by complementing ASP's declarative input language with control capacities. On the declarative side, a new directive allows for structuring logic programs into named and parameterizable subprograms. The grounding and integration of these subprograms into the solving process is completely modular and fully controllable from the procedural side. To this end,clingooffers a new application programming interface that is conveniently accessible via scripting languages. By strictly separating logic and control,clingoalso abolishes the need for dedicated systems for incremental and reactive reasoning, likeiclingoandoclingo, respectively, and its flexibility goes well beyond the advanced yet still rigid solving processes of the latter.

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13

Pawlak, Tomasz P. "Synthesis of Mathematical Programming models with one-class evolutionary strategies." Swarm and Evolutionary Computation 44 (February 2019): 335–48. http://dx.doi.org/10.1016/j.swevo.2018.04.007.

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14

Singh, Nirbhow Jap, Shakti Singh, Vikram Chopra, Mohd Asim Aftab, S. M. Suhail Hussain, and Taha Selim Ustun. "Chaotic Evolutionary Programming for an Engineering Optimization Problem." Applied Sciences 11, no. 6 (March 18, 2021): 2717. http://dx.doi.org/10.3390/app11062717.

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The aim of the current paper is to present a mimetic algorithm called the chaotic evolutionary programming Powell’s pattern search (CEPPS) algorithm for the solution of the multi-fuel economic load dispatch problem. In the CEPPS algorithm, the exploration process is maintained by chaotic evolutionary programming, whereas exploitation is taken care off by a pattern search. The proposed CEPPS has two variants based on the Gauss map and the tent map. Seven generalized benchmark test functions and six cases of the multi-fuel economic load dispatch problem are considered for the performance analysis. It is observed from the analysis that the CEPPS solution procedure based on the tent map exhibits superiority to obtain an excellent solution and better convergence characteristics than traditional chaotic evolutionary programming. Further, the performance investigation for the considered economic load dispatch shows that the Gauss map CEPPS solution procedure performs better than the tent map based CEPPS to obtain the solution of the multi-fuel economic dispatch problem.

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15

Olhoff, N. "Multicriterion structural optimization via bound formulation and mathematical programming." Structural Optimization 1, no. 1 (March 1989): 11–17. http://dx.doi.org/10.1007/bf01743805.

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16

Cakir, Merve Nur, Mehwish Saleemi, and Karl-Heinz Zimmermann. "Dynamic Programming in Topological Spaces." WSEAS TRANSACTIONS ON COMPUTERS 20 (June 25, 2021): 88–91. http://dx.doi.org/10.37394/23205.2021.20.11.

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Dynamic programming is a mathematical optimization method and a computer programming method as well. In this paper, the notion of sheaf programming in topological spaces is introduced and it is demonstrated that it relates very well to the concept of dynamic programming.

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17

Stanojević, Bogdana, Simona Dzitac, and Ioan Dzitac. "Fuzzy Numbers and Fractional Programming in Making Decisions." International Journal of Information Technology & Decision Making 19, no. 04 (July 2020): 1123–47. http://dx.doi.org/10.1142/s0219622020300037.

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This study surveys the use of fuzzy numbers in classic optimization models, and its effects on making decisions. In a wide sense, mathematical programming is a collection of tools used in mathematical optimization to make good decisions. There are many sectors of economy that employ it. Finance and government, logistics and manufacturing, the distribution of the electrical power are worth to be first mentioned. When real life problems are modeled mathematically, there is always a trade-off between model’s accuracy and complexity. By this survey, we aim to present in a concise form some mathematical models from the literature together with the methods to solve them. We will focus mainly on fuzzy fractional programming problems. We will also refer to but not describe in detail the multi-criteria decision-making problems involving fuzzy numbers and linear fractional programming models.

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Fang, Weijian, Guangwen Xi, and Xiaoling Tan. "Optimization strategy of computer programming for mathematical algorithm of facial recognition model." Journal of Computational Methods in Sciences and Engineering 19 (August 14, 2019): 19–25. http://dx.doi.org/10.3233/jcm-191003.

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19

Herskovits, J., P. Mappa, E. Goulart, and C. M. Mota Soares. "Mathematical programming models and algorithms for engineering design optimization." Computer Methods in Applied Mechanics and Engineering 194, no. 30-33 (August 2005): 3244–68. http://dx.doi.org/10.1016/j.cma.2004.12.017.

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20

FREY, CLEMENS. "CO-EVOLUTION OF FINITE STATE MACHINES FOR OPTIMIZATION: PROMOTION OF DEVICES WHICH SEARCH GLOBALLY." International Journal of Computational Intelligence and Applications 02, no. 01 (March 2002): 1–16. http://dx.doi.org/10.1142/s1469026802000397.

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In this work a co-evolutionary approach is used in conjunction with Genetic Programming operators in order to find certain transition rules for two-step discrete dynamical systems. This issue is similar to the well-known artificial-ant problem. We seek the dynamic system to produce a trajectory leading from given initial values to a maximum of a given spatial functional.This problem is recast into the framework of input-output relations for controllers, and the optimization is performed on program trees describing input filters and finite state machines incorporated by these controllers simultaneously. In the context of Genetic Programming there is always a set of test cases which has to be maintained for the evaluation of program trees. These test cases are subject to evolution here, too, so we employ a so-called host-parasitoid model in order to evolve optimizing dynamical systems.Reinterpreting these systems as algorithms for finding the maximum of a functional under constraints, we have derived a paradigm for the automatic generation of adapted optimization algorithms via optimal control. We provide numerical examples generated by the GP-system MathEvEco. These examples refer to key properties of the resulting strategies and they include statistical evidence showing that for this problem of system identification the co-evolutionary approach is superior to standard Genetic Programming.

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WAH, BENJAMIN W., and YIXIN CHEN. "HYBRID EVOLUTIONARY AND ANNEALING ALGORITHMS FOR NONLINEAR DISCRETE CONSTRAINED OPTIMIZATION." International Journal of Computational Intelligence and Applications 03, no. 04 (December 2003): 331–55. http://dx.doi.org/10.1142/s1469026803001063.

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This paper presents a procedural framework that unifies various mechanisms to look for discrete-neighborhood saddle points in solving discrete constrained optimization problems (DCOPs). Our approach is based on the necessary and sufficient condition on local optimality in discrete space, which shows the one-to-one correspondence between the discrete-space constrained local minima of a problem and the saddle points of the corresponding Lagrangian function. To look for such saddle points, we study various mechanisms for performing ascents of the Lagrangian function in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our results show that CSAEA, a combined constrained simulated annealing and evolutionary algorithm, performs well when using mutations and crossovers to generate trial points and accepting them based on the Metropolis probability. We apply iterative deepening to determine the optimal number of generations in CSAEA and show that its performance is robust with respect to changes in population size. To test the performance of the procedures developed, we apply them to solve some continuous and mixed-integer nonlinear programming (NLP) benchmarks and show that they obtain better results than those of existing algorithms.

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Alam, Mohammad Shafiul, Md Monirul Islam, Xin Yao, and Kazuyuki Murase. "Recurring Two-Stage Evolutionary Programming: A Novel Approach for Numeric Optimization." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41, no. 5 (October 2011): 1352–65. http://dx.doi.org/10.1109/tsmcb.2011.2144968.

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23

Perez, Hector D., Satyajith Amaran, Esra Erisen, John M. Wassick, and Ignacio E. Grossmann. "Optimization of extended business processes in digital supply chains using mathematical programming." Computers & Chemical Engineering 152 (September 2021): 107323. http://dx.doi.org/10.1016/j.compchemeng.2021.107323.

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Ravichandran, Sathish Kumar, Archana Sasi, and Shaik Hussain Shaik Ibrahim. "Forest Optimization Algorithm Implementation using Sphere Mathematical Function." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 104–10. http://dx.doi.org/10.35940/ijeat.e2552.0610521.

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Forest Optimization Algorithm (FOA), a recent evolutionary algorithm suitable for continuous nonlinear optimization problems. It is inspired by a few trees in the forest that can last for several decades while other trees can only live for a short time. In FOA, the tree seeding technique is simulated so that certain seeds fall directly under the leaves, while others are dispersed over a large area by natural processes and animals that feed on the seeds or fruits. In this paper, we used the sphere mathematical function to implement FOA as a step-by-step process, and the iteration-based results are displayed. The findings of the experiments demonstrated that FOA performed well in certain data sets from the UCI repository.

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Antczak, Tadeusz. "Generalized (p, r)-Invexity in Mathematical Programming." Numerical Functional Analysis and Optimization 24, no. 5-6 (January 12, 2003): 437–53. http://dx.doi.org/10.1081/nfa-120023865.

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Singh, Vishnu, and Shiv Prasad Yadav. "Development and optimization of unrestricted LR-type intuitionistic fuzzy mathematical programming problems." Expert Systems with Applications 80 (September 2017): 147–61. http://dx.doi.org/10.1016/j.eswa.2017.03.015.

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Gil-González, Walter, Oscar Danilo Montoya, Luis Fernando Grisales-Noreña, Fernando Cruz-Peragón, and Gerardo Alcalá. "Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization." Energies 13, no. 7 (April 3, 2020): 1703. http://dx.doi.org/10.3390/en13071703.

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A convex mathematical model based on second-order cone programming (SOCP) for the optimal operation in direct current microgrids (DCMGs) with high-level penetration of renewable energies and battery energy storage systems (BESSs) is developed in this paper. The SOCP formulation allows converting the non-convex model of economic dispatch into a convex approach that guarantees the global optimum and has an easy implementation in specialized software, i.e., CVX. This conversion is accomplished by performing a mathematical relaxation to ensure the global optimum in DCMG. The SOCP model includes changeable energy purchase prices in the DCMG operation, which makes it in a suitable formulation to be implemented in real-time operation. An energy short-term forecasting model based on a receding horizon control (RHC) plus an artificial neural network (ANN) is used to forecast primary sources of renewable energy for periods of 0.5h. The proposed mathematical approach is compared to the non-convex model and semidefinite programming (SDP) in three simulation scenarios to validate its accuracy and efficiency.

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BAUGH, JOHN W., SHANNON C. CALDWELL, and E. DOWNEY BRILL. "A MATHEMATICAL PROGRAMMING APPROACH FOR GENERATING ALTERNATIVES IN DISCRETE STRUCTURAL OPTIMIZATION." Engineering Optimization 28, no. 1-2 (May 1997): 1–31. http://dx.doi.org/10.1080/03052159708941125.

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Rocca, P., M. Benedetti, M. Donelli, D. Franceschini, and A. Massa. "Evolutionary optimization as applied to inverse scattering problems." Inverse Problems 25, no. 12 (November 23, 2009): 123003. http://dx.doi.org/10.1088/0266-5611/25/12/123003.

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Li, Wenhua, Guo Zhang, Xu Yang, Zhang Tao, and Hu Xu. "Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm." Complexity 2021 (February 10, 2021): 1–9. http://dx.doi.org/10.1155/2021/8822765.

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Hybrid renewable energy system (HRES) arises regularly in real life. By optimizing the capacity and running status of the microgrid (MG), HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, we first proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems.

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Stanojević, Bogdana, Milan Stanojević, and Sorin Nădăban. "Reinstatement of the Extension Principle in Approaching Mathematical Programming with Fuzzy Numbers." Mathematics 9, no. 11 (June 1, 2021): 1272. http://dx.doi.org/10.3390/math9111272.

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Optimization problems in the fuzzy environment are widely studied in the literature. We restrict our attention to mathematical programming problems with coefficients and/or decision variables expressed by fuzzy numbers. Since the review of the recent literature on mathematical programming in the fuzzy environment shows that the extension principle is widely present through the fuzzy arithmetic but much less involved in the foundations of the solution concepts, we believe that efforts to rehabilitate the idea of following the extension principle when deriving relevant fuzzy descriptions to optimal solutions are highly needed. This paper identifies the current position and role of the extension principle in solving mathematical programming problems that involve fuzzy numbers in their models, highlighting the indispensability of the extension principle in approaching this class of problems. After presenting the basic ideas in fuzzy optimization, underlying the advantages and disadvantages of different solution approaches, we review the main methodologies yielding solutions that elude the extension principle, and then compare them to those that follow it. We also suggest research directions focusing on using the extension principle in all stages of the optimization process.

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Lin, Lai-Jiu. "Mathematical Programming with System of Equilibrium Constraints." Journal of Global Optimization 37, no. 2 (July 6, 2006): 275–86. http://dx.doi.org/10.1007/s10898-006-9049-5.

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Grimstad, Bjarne, and Brage R. Knudsen. "Mathematical programming formulations for piecewise polynomial functions." Journal of Global Optimization 77, no. 3 (February 3, 2020): 455–86. http://dx.doi.org/10.1007/s10898-020-00881-4.

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Pekár, Juraj, Ivan Brezina, Jaroslav Kultan, Iryna Ushakova, and Oleksandr Dorokhov. "Computer tools for solving the traveling salesman problem." Development Management 18, no. 1 (June 30, 2020): 25–39. http://dx.doi.org/10.21511/dm.18(1).2020.03.

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The task of the traveling salesman, which is to find the shortest or least costly circular route, is one of the most common optimization problems that need to be solved in various fields of practice. The article analyzes and demonstrates various methods for solving this problem using a specific example: heuristic (the nearest neighbor method, the most profitable neighbor method), metaheuristic (evolutionary algorithm), methods of mathematical programming. In addition to classic exact methods (which are difficult to use for large-scale tasks based on existing software) and heuristic methods, the article suggests using the innovative features of the commercially available MS Excel software using a meta-heuristic base. To find the optimal solution using exact methods, the Excel (Solver) software package was used, as well as the specialized GAMS software package. Comparison of different approaches to solving the traveling salesman problem using a practical example showed that the use of traditional heuristic approaches (the nearest neighbor method or the most profitable neighbor method) is not difficult from a computational point of view, but does not provide solutions that would be acceptable in modern conditions. The use of MS Excel for solving the problem using the methods of mathematical programming and metaheuristics enabled us to obtain an optimal solution, which led to the conclusion that modern tools are an appropriate addition to solving the traveling salesman problem while maintaining the quality of the solution.

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Saxena, Pratiksha, and Ravi Jain. "Bector-Chandra Type Duality in Linear Programming Under Fuzzy Environment Using Hyperbolic Tangent Membership Functions." International Journal of Fuzzy System Applications 8, no. 2 (April 2019): 68–88. http://dx.doi.org/10.4018/ijfsa.2019040104.

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Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. One approach to optimize a multi-objective mathematical model is to employ utility functions for the objectives. Recent studies on utility-based multi-objective optimization concentrates on considering just one utility function for each objective. But, in reality, it is not reasonable to have a unique utility function corresponding to each objective function. Here, a constrained multi-objective mathematical model is considered in which several utility functions are associated for each objective. All of these utility functions are uncertain and in fuzzy form, so a fuzzy probabilistic approach is incorporated to investigate the uncertainty of the utility functions for each objective. Meanwhile, the total utility function of the problem will be a fuzzy nonlinear mathematical model. Since there are not any conventional approaches to solve such a model, a defuzzification method to change the total utility function to a crisp nonlinear model is employed. Also, a maximum technique is applied to defuzzify the conditional utility functions. This action results in changing the total utility function to a crisp single objective nonlinear model and will simplify the optimization process of the total utility function. The effectiveness of the proposed approach is shown by solving a test problem.

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ERDEM, ESRA, VLADIMIR LIFSCHITZ, and DON RINGE. "Temporal phylogenetic networks and logic programming." Theory and Practice of Logic Programming 6, no. 5 (August 2, 2006): 539–58. http://dx.doi.org/10.1017/s1471068406002729.

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The concept of a temporal phylogenetic network is a mathematical model of evolution of a family of natural languages. It takes into account the fact that languages can trade their characteristics with each other when linguistic communities are in contact, and also that a contact is only possible when the languages are spoken at the same time. We show how computational methods of answer set programming and constraint logic programming can be used to generate plausible conjectures about contacts between prehistoric linguistic communities, and illustrate our approach by applying it to the evolutionary history of Indo-European languages.

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Izadkhah, Habib, Islam Elgedawy, and Ayaz Isazadeh. "E-CDGM: An Evolutionary Call-Dependency Graph Modularization Approach for Software Systems." Cybernetics and Information Technologies 16, no. 3 (September 1, 2016): 70–90. http://dx.doi.org/10.1515/cait-2016-0035.

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Abstract Lack of up-to-date software documentation hinders the software evolution and maintenance processes, as simply the outdated software structure and code could be easily misunderstood. One approach to overcoming such problems is using software modularization, in which the software architecture is extracted from the available source code; such that developers can assess the reconstructed architecture against the required changes. Unfortunately, existing software modularization approaches are not accurate, as they ignore polymorphic calls among system modules. Furthermore, they are tightly coupled to the used programming language. To overcome such problems, this paper proposes the E-CDGM approach. E-CDGM decouples the extracted call dependency graph from the programming language by using the proposed intermediate code language (known as mCode). It also takes into consideration the polymorphic calls during the call dependency graph generation. It uses a new evolutionary optimization approach to find the best modularization option; adopting reward and penalty functions. Finally, it uses statistical analysis to build a final consolidated modularization model using different generated modularization solutions. Experimental results show that the proposed E-CDGM approach provides more accurate results when compared against existing well-known modularization approaches.

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Antczak, Tadeusz. "GeneralizedB-(p,r)-Invexity Functions and Nonlinear Mathematical Programming." Numerical Functional Analysis and Optimization 30, no. 1-2 (February 20, 2009): 1–22. http://dx.doi.org/10.1080/01630560802678549.

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Tenek, Lazarus H., and Ichiro Hagiwara. "Static and vibrational shape and topology optimization using homogenization and mathematical programming." Computer Methods in Applied Mechanics and Engineering 109, no. 1-2 (January 1993): 143–54. http://dx.doi.org/10.1016/0045-7825(93)90229-q.

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TIN-LOI, F. "PLASTIC LIMIT ANALYSIS, MATHEMATICAL PROGRAMMING AND GAMS." Engineering Optimization 20, no. 4 (February 1993): 273–86. http://dx.doi.org/10.1080/03052159308941285.

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Luo, Xiong, Xiaoping Fan, Heng Zhang, and Tefang Chen. "Novel integrated optimization algorithm for trajectory planning of robot manipulators based on integrated evolutionary programming." Journal of Control Theory and Applications 2, no. 4 (November 2004): 319–31. http://dx.doi.org/10.1007/s11768-004-0035-5.

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Jooshaki, Mohammad, Ali Abbaspour, Mahmud Fotuhi-Firuzabad, Moein Moeini-Aghtaie, and Matti Lehtonen. "Multistage Expansion Co-Planning of Integrated Natural Gas and Electricity Distribution Systems." Energies 12, no. 6 (March 15, 2019): 1020. http://dx.doi.org/10.3390/en12061020.

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This paper focuses on expansion co-planning studies of natural gas and electricity distribution systems. The aim is to develop a mixed-integer linear programming (MILP) model for such problems to guarantee the finite convergence to optimality. To this end, at first the interconnection of electricity and natural gas networks at demand nodes is modelled by the concept of energy hub (EH). Then, mathematical model of expansion studies associated with the natural gas, electricity and EHs are extracted. The optimization models of these three expansion studies incorporate investment and operation costs. Based on these separate planning problems, which are all in the form of mixed-integer nonlinear programming (MINLP), joint expansion model of multi-carrier energy distribution system is attained and linearized to form a MILP optimization formulation. The presented optimization framework is illustratively applied to an energy distribution network and the results are discussed.

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Dehghani, Ehsan, Mir Saman Pishvaee, and Mohammad Saeed Jabalameli. "A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions." RAIRO - Operations Research 52, no. 4-5 (October 2018): 1147–73. http://dx.doi.org/10.1051/ro/2018012.

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This paper introduces a joint location-inventory problem, in which facilities become temporarily unavailable. A hybrid approach based on the Markov process and mathematical programming techniques is presented to design the distribution network of a supply chain in an integrated manner. In the first phase, the Markov process derives some performance features of inventory policy. In the second phase, using outputs of the Markov process, the location-inventory problem is formulated as a mixed-integer nonlinear programming model. Moreover, a robust possibilistic programming approach is utilized, which is able to provide a more stable supply chain structure under almost all possible values of imprecise parameters. Since the proposed problem is complicated to solve by means of exact methods, we develop a simulated annealing algorithm in order to find near-optimal solutions in reasonable computational times. The obtained computational results reveal the efficiency and effectiveness of the proposed solution approach. Finally, some insights are provided and the performance of the proposed robust optimization approach is compared to traditional possibilistic chance constrained method.

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Sahin, Bekir, Devran Yazir, Abdelsalam Adam Hamid, and Noorul Shaiful Fitri Abdul Rahman. "Maritime Supply Chain Optimization by Using Fuzzy Goal Programming." Algorithms 14, no. 8 (August 9, 2021): 234. http://dx.doi.org/10.3390/a14080234.

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Fuzzy goal programming has important applications in many areas of supply chain, logistics, transportation and shipping business. Business management has complications, and there exist many interactions between the factors of its components. The locomotive of world trade is maritime transport and approximately 90% of the products in the world are transported by sea. Optimization of maritime operations is a challenge in order to provide technical, operational and financial benefits. Fuzzy goal programming models attract interests of many scholars, therefore the objective of this paper is to investigate the problem of minimization of total cost and minimization of loss or damage of containers returned from destination port. There are various types of fuzzy goal programming problems based on models and solution methods. This paper employs fuzzy goal programming with triangular fuzzy numbers, membership functions, constraints, assumptions as well as the variables and parameters for optimizing the solution of the model problem. The proposed model presents the mathematical algorithm, and reveals the optimal solution according to satisfaction rank from 0 to 1. Providing a theoretical background, this study offers novel ideas to researchers, decision makers and authorities.

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Klemmt, Andreas, Sven Horn, Gerald Weigert, and Klaus-Jürgen Wolter. "Simulation-based optimization vs. mathematical programming: A hybrid approach for optimizing scheduling problems." Robotics and Computer-Integrated Manufacturing 25, no. 6 (December 2009): 917–25. http://dx.doi.org/10.1016/j.rcim.2009.04.012.

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Ferrari, Paolo. "A Three-Level Mathematical Programming Model of Road Pricing." Journal of Global Optimization 28, no. 3/4 (April 2004): 297–304. http://dx.doi.org/10.1023/b:jogo.0000026450.28155.95.

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Flores-Bazán, Fabián, Nicolas Hadjisavvas, and Cristián Vera. "An Optimal Alternative Theorem and Applications to Mathematical Programming." Journal of Global Optimization 37, no. 2 (June 27, 2006): 229–43. http://dx.doi.org/10.1007/s10898-006-9046-8.

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Xidonas, Panagiotis, George Mavrotas, and John Psarras. "Equity portfolio construction and selection using multiobjective mathematical programming." Journal of Global Optimization 47, no. 2 (August 18, 2009): 185–209. http://dx.doi.org/10.1007/s10898-009-9465-4.

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Asokan, P., N. Baskar, K. Babu, G. Prabhaharan, and R. Saravanan. "Optimization of Surface Grinding Operations Using Particle Swarm Optimization Technique." Journal of Manufacturing Science and Engineering 127, no. 4 (January 11, 2005): 885–92. http://dx.doi.org/10.1115/1.2037085.

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The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many others. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. In this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques.

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Bakushinsky, A. B., and T. A. Matijasevich. "Some applications of the iterative regularization principle to mathematical programming problems." Numerical Functional Analysis and Optimization 10, no. 1-2 (January 1989): 1–13. http://dx.doi.org/10.1080/01630568908816287.

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