Relevant bibliographies by topics / Constraint Handling Techniques

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Constraint Handling Techniques'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Constraint Handling Techniques"

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LIU, BING. "SPECIFIC CONSTRAINT HANDLING IN CONSTRAINT SATISFACTION PROBLEMS." International Journal on Artificial Intelligence Tools 03, no. 01 (1994): 79–96. http://dx.doi.org/10.1142/s0218213094000066.

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Abundant literatures exist on consistency techniques for solving Constraint Satisfaction Problems (CSPs). These literatures, however, focused mainly on finding efficient general techniques to achieve network consistency and to solve CSPs. So far, many techniques have been reported, e.g., node consistency, arc consistency, path consistency, k-consistency, forward checking, lookahead, partial lookahead, etc. Not enough attention has been given to individual constraints, and how constraint specific features may be exploited for more efficient consistency check. Many types of constraints exist in
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Kramer, Oliver. "A Review of Constraint-Handling Techniques for Evolution Strategies." Applied Computational Intelligence and Soft Computing 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/185063.

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Evolution strategies are successful global optimization methods. In many practical numerical problems constraints are not explicitly given. Evolution strategies have to incorporate techniques to optimize in restricted solution spaces. Famous constraint-handling techniques are penalty and multiobjective approaches. Past work has shown that in particular an ill-conditioned alignment between the coordinate system of Gaussian mutation and the constraint boundaries leads to premature convergence. Covariance matrix adaptation evolution strategies offer a solution to this alignment problem. Last, met
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Mallipeddi, R., and Ponnuthurai N. Suganthan. "Ensemble of Constraint Handling Techniques." IEEE Transactions on Evolutionary Computation 14, no. 4 (2010): 561–79. http://dx.doi.org/10.1109/tevc.2009.2033582.

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Nordin, Axel, Damien Motte, Andreas Hopf, Robert Bjärnemo, and Claus-Christian Eckhardt. "Constraint-handling techniques for generative product design systems in the mass customization context." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, no. 4 (2013): 387–99. http://dx.doi.org/10.1017/s0890060413000383.

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AbstractGenerative product design systems used in the context of mass customization are required to generate diverse solutions quickly and reliably without necessitating modification or tuning during use. When such systems are employed to allow for the mass customization of product form, they must be able to handle mass production and engineering constraints that can be time-consuming to evaluate and difficult to fulfill. These issues are related to how the constraints are handled in the generative design system. This article evaluates two promising sequential constraint-handling techniques an
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Yang, Ning, and Hai-Lin Liu. "Adaptively Allocating Constraint-Handling Techniques for Constrained Multi-objective Optimization Problems." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 08 (2021): 2159032. http://dx.doi.org/10.1142/s0218001421590321.

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For solving constrained multi-objective optimization problems (CMOPs), an effective constraint-handling technique (CHT) is of great importance. Recently, many CHTs have been proposed for solving CMOPs. However, no single CHT can outperform all kinds of CMOPs. This paper proposes an algorithm, namely, ACHT-M2M, which adaptively allocates the existing CHTs in an M2M framework for solving CMOPs. To be more specific, a CMOP is first decomposed into several constrained multi-objective optimization subproblems by ACHT-M2M. Each subproblem has a subpopulation in a subregion. CHT for each subregion is
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Malan, Katherine M., and I. Moser. "Constraint Handling Guided by Landscape Analysis in Combinatorial and Continuous Search Spaces." Evolutionary Computation 27, no. 2 (2019): 267–89. http://dx.doi.org/10.1162/evco_a_00222.

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The notion and characterisation of fitness landscapes has helped us understand the performance of heuristic algorithms on complex optimisation problems. Many practical problems, however, are constrained, and when significant areas of the search space are infeasible, researchers have intuitively resorted to a variety of constraint-handling techniques intended to help the algorithm manoeuvre through infeasible areas and toward feasible regions of better fitness. It is clear that providing constraint-related feedback to the algorithm to influence its choice of solutions overlays the violation lan
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Javed, Hassan, Muhammad Asif Jan, Nasser Tairan, et al. "On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution." Mathematics 7, no. 7 (2019): 635. http://dx.doi.org/10.3390/math7070635.

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Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution with optional external archive (JADE) and self-adaptive differential evolution (SaDE) are two well-known self-adaptive versions of differential evolution (DE). They are both unconstrained search and optimization algorithms. However, if some constraint handling techniques (CHTs) are incorporated in their frameworks, then they can be used to solve constrained optimization problems (COPs). In an early work, an ensemble of constraint handling
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Cinar, Ahmet, and Mustafa Kiran. "The Performance of Penalty Methods on Tree-Seed Algorithm for Numerical Constrained Optimization Problems." International Arab Journal of Information Technology 17, no. 5 (2020): 799–807. http://dx.doi.org/10.34028/iajit/17/5/13.

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The constraints are the most important part of many optimization problems. The metaheuristic algorithms are designed for solving continuous unconstrained optimization problems initially. The constraint handling methods are integrated into these algorithms for solving constrained optimization problems. Penalty approaches are not only the simplest way but also as effective as other constraint handling techniques. In literature, there are many penalty approaches and these are grouped as static, dynamic and adaptive. In this study, we collect them and discuss the key benefits and drawbacks of thes
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Villa-Acevedo, Walter, Jesús López-Lezama, and Jaime Valencia-Velásquez. "A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem." Energies 11, no. 9 (2018): 2352. http://dx.doi.org/10.3390/en11092352.

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This paper presents an alternative constraint handling approach within a specialized genetic algorithm (SGA) for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a nonlinear single-objective optimization problem aiming at minimizing power losses while keeping network constraints. The proposed constraint handling approach is based on a product of sub-functions that represents permissible limits on system variables and that includes a specific goal on power loss reduction. The main advantage of this approach is the fact that it allows a straightforward verification o
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Carvalho, Érica Da Costa Reis, José Pedro Gonçalves Carvalho, Heder Soares Bernardino, Patrícia Habib Hallak, and Afonso Celso de Castro Lemonge. "AN ADAPTIVE CONSTRAINT HANDLING TECHNIQUE FOR PARTICLE SWARM IN CONSTRAINED OPTIMIZATION PROBLEMS." Revista CIATEC-UPF 8, no. 1 (2016): 39. http://dx.doi.org/10.5335/ciatec.v8i1.6023.

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Nature inspired meta-heuristics are largely used to solve optimization problems. However, these techniques should be adapted when solving constrained optimization problems, which are common in real world situations. Here an adaptive penalty approach (called Adaptive Penalty Method, APM) is combined with a particle swarm optimization (PSO) technique to solve constrained optimization problems. This approach is analyzed using a benchmark of test-problems and 5 mechanical engineering problems. Moreover, three variants of APM are considered in the computational experiments. Comparison results show
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Dissertations / Theses on the topic "Constraint Handling Techniques"

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Vargas, Dênis Emanuel da Costa. "Um algoritmo de evolução diferencial com penalização adaptativa para otimização estrutural multiobjetivo." Universidade Federal de Juiz de Fora, 2015. https://repositorio.ufjf.br/jspui/handle/ufjf/418.

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Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-15T14:16:25Z No. of bitstreams: 1 denisemanueldacostavargas.pdf: 16589539 bytes, checksum: 44a0869db27ffd5f8254f85fb69ab78c (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T17:40:31Z (GMT) No. of bitstreams: 1 denisemanueldacostavargas.pdf: 16589539 bytes, checksum: 44a0869db27ffd5f8254f85fb69ab78c (MD5)<br>Made available in DSpace on 2016-01-25T17:40:31Z (GMT). No. of bitstreams: 1 denisemanueldacostavargas.pdf: 16589539 bytes, checksum: 44a0869db27ffd5f8254f85fb69ab78c (M
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Barkat, Ullah Abu Saleh Shah Muhammad Engineering &amp Information Technology Australian Defence Force Academy UNSW. "An integrated evolutionary system for solving optimization problems." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43764.

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Many real-world decision processes require solving optimization problems which may involve different types of constraints such as inequality and equality constraints. The hurdles in solving these Constrained Optimization Problems (COPs) arise from the challenge of searching a huge variable space in order to locate feasible points with acceptable solution quality. Over the last decades Evolutionary Algorithms (EAs) have brought a tremendous advancement in the area of computer science and optimization with their ability to solve various problems. However, EAs have inherent difficulty in d
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Su, Yu-De, and 蘇育德. "Multiobjective Technique in Constraints Handling GA for Electro-Thermal Microactuator Optimization." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/97146683371583018574.

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碩士<br>淡江大學<br>機械與機電工程學系碩士班<br>99<br>The quantity of violation in constraints is simulated to the concept of gene pathological change in biological system. The violation of all constraints is transformed to an artificially second design objective required to be minimized simultaneously. Consequently, the original objective integrated with such artificial objective to construct a two-objective in design optimization problem. A unique Pareto point can be sequentially obtained in which the value of second objective will converge to zero. This thesis presents the complete numerical algorithm and pr
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Book chapters on the topic "Constraint Handling Techniques"

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Michalewicz, Zbigniew, and David B. Fogel. "Constraint-Handling Techniques." In How to Solve It: Modern Heuristics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07807-5_10.

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Michalewicz, Zbigniew, and David B. Fogel. "Constraint-Handling Techniques." In How to Solve It: Modern Heuristics. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04131-4_10.

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Lopez-Sanchez, Misael, M. A. Cosío-León, and Anabel Martínez-Vargas. "Comparative Analysis of Constraint Handling Techniques Based on Taguchi Design of Experiments." In Constraint Handling in Metaheuristics and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6710-4_14.

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Mallipeddi, Rammohan, Swagatam Das, and Ponnuthurai Nagaratnam Suganthan. "Ensemble of Constraint Handling Techniques for Single Objective Constrained Optimization." In Infosys Science Foundation Series. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2184-5_9.

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He, Xing-Shi, Qin-Wei Fan, Mehmet Karamanoglu, and Xin-She Yang. "Comparison of Constraint-Handling Techniques for Metaheuristic Optimization." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22744-9_28.

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Si, Chengyong, Jianqiang Shen, Weian Guo, and Lei Wang. "On the Cooperation Between Evolutionary Algorithms and Constraint Handling Techniques." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93815-8_5.

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Marusak, Piotr M. "Efficient Mechanism of Output Constraint Handling for Analytical Predictive Controllers Based on Hammerstein Models." In Recent Advances in Automation, Robotics and Measuring Techniques. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05353-0_14.

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Fukumoto, Hiroaki, and Akira Oyama. "A Generic Framework for Incorporating Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms." In Applications of Evolutionary Computation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77538-8_43.

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Oldewage, Elre T., Andries P. Engelbrecht, and Christopher W. Cleghorn. "Boundary Constraint Handling Techniques for Particle Swarm Optimization in High Dimensional Problem Spaces." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00533-7_27.

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Barbosa, Helio J. C., Afonso C. C. Lemonge, Leonardo G. Fonseca, and Heder S. Bernardino. "Comparing Two Constraint Handling Techniques in a Binary-Coded Genetic Algorithm for Optimization Problems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17298-4_13.

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Conference papers on the topic "Constraint Handling Techniques"

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Si, Chengyong, Lei Wang, and Qidi Wu. "Mapping constrained optimization problems to algorithms and constraint handling techniques." In 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012. http://dx.doi.org/10.1109/cec.2012.6252974.

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Coello Coello, Carlos Artemio. "Constraint-handling techniques used with evolutionary algorithms." In the 13th annual conference companion. ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2002130.

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Coello Coello, Carlos Artemio. "Constraint-handling techniques used with evolutionary algorithms." In the 11th annual conference companion. ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570413.

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Coello Coello, Carlos Artemio. "Constraint-handling techniques used with evolutionary algorithms." In the fourteenth international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330920.

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Coello Coello, Carlos A. "Constraint-handling techniques used with evolutionary algorithms." In the 2008 GECCO conference companion. ACM Press, 2008. http://dx.doi.org/10.1145/1388969.1389063.

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Coello, Carlos A. Coello. "Constraint-handling techniques used with evolutionary algorithms." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3207855.

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Coello Coello, Carlos A. "Constraint-Handling Techniques used with Evolutionary Algorithms." In GECCO '16: Genetic and Evolutionary Computation Conference. ACM, 2016. http://dx.doi.org/10.1145/2908961.2926986.

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Coello Coello, Carlos Artemio. "Constraint-handling techniques used with evolutionary algorithms." In Proceeding of the fifteenth annual conference companion. ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2480802.

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Coello Coello, Carlos A. "Constraint-handling techniques used with evolutionary algorithms." In the 12th annual conference comp. ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830910.

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Coello Coello, Carlos A. "Constraint-handling techniques used with evolutionary algorithms." In the 2007 GECCO conference companion. ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274105.

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