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Journal articles on the topic 'Algoritmi memetici'

Author: Grafiati

Published: 10 March 2023

Last updated: 19 July 2025

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1

Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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 One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorit

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Dodu, A. Y. Erwin, Deny Wiria Nugraha, and Subkhan Dinda Putra. "Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika." JURNAL SISTEM INFORMASI BISNIS 8, no. 1 (2018): 99. http://dx.doi.org/10.21456/vol8iss1pp99-106.

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The problem of midwife scheduling is one of the most frequent problems in hospitals. Midwife should be available 24 hours a day for a full week to meet the needs of the patient. Therefore, good or bad midwife scheduling result will have an impact on the quality of care on the patient and the health of the midwife on duty. The midwife scheduling process requires a lot of time, effort and good cooperation between some parties to solve this problem that is often faced by the Regional Public Hospital Undata Palu Central Sulawesi Province. This research aimed to apply Memetics algorithm to make sch

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Handa, Hisashi. "Solving Constraint Satisfaction Problems by Memetic Algorithms Using Estimation of Distribution Algorithms." Transactions of the Japanese Society for Artificial Intelligence 19 (2004): 405–12. http://dx.doi.org/10.1527/tjsai.19.405.

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He Zewen, 何泽文, 庄秋实 Zhuang Qiushi, 曹惠宁 Cao Huining та 辛煜 Xin Yu. "基于文化基因算法的透过散射介质聚焦". Laser & Optoelectronics Progress 58, № 24 (2021): 2429001. http://dx.doi.org/10.3788/lop202158.2429001.

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Vairam, Senthil, and V. Selladurai. "Parallel Machine Shop Scheduling Using Memetic Algorithm." Applied Mechanics and Materials 573 (June 2014): 362–67. http://dx.doi.org/10.4028/www.scientific.net/amm.573.362.

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Parallel machine shop scheduling problem can be stated as finding a schedule for a general task graph to execute on a customed flow so that the schedule length can be minimized. Parallel Flow Shop Scheduling with a case study has been . In this study we present an effective memetic algorithm to solve the problem. Also evaluating the performance of two algorithms (genetic algorithm and memetic algorithm) in terms of both the quality of the solutions produced and the efficiency. These results demonstrate that the memetic algorithm produces better and quality solutions and hence it is very effici

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Phan, Tuan Anh, and Anh Tuan Duong. "A FRAMEWORK FOR MEMETIC ALGORITHMS." Science and Technology Development Journal 12, no. 11 (2009): 27–38. http://dx.doi.org/10.32508/stdj.v12i11.2309.

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Memetic algorithm, a combination of genetic algorithm with local search, is one of the most successful metaheuristics to solve complex combinatorial optimization problems. In this paper, we will introduce an object-oriented framework which allows the construction of memetic algorithms with a maximum reuse. This framework has been developed in Java using design patterns to allow its easy extension and utilization in different problem domains. Our framework has been experimented through the development of a memetic algorithm for solving set covering problems.

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Elleuch, Souhir, and Bassem Jarboui. "Improved memetic programming algorithm." International Journal of Operational Research 44, no. 3 (2022): 389. http://dx.doi.org/10.1504/ijor.2022.124105.

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Elleuch, Souhir, and Bassem Jarboui. "Improved Memetic Programming algorithm." International Journal of Operational Research 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijor.2021.10032941.

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Vakil-Baghmisheh, M. T., and Morteza Alinia Ahandani. "A differential memetic algorithm." Artificial Intelligence Review 41, no. 1 (2012): 129–46. http://dx.doi.org/10.1007/s10462-011-9302-2.

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Molina, Daniel, Manuel Lozano, Carlos García-Martínez, and Francisco Herrera. "Memetic Algorithms for Continuous Optimisation Based on Local Search Chains." Evolutionary Computation 18, no. 1 (2010): 27–63. http://dx.doi.org/10.1162/evco.2010.18.1.18102.

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Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, on some occasions, they may become very expensive, because of the way they exploit local information to guide the search process. In this paper, they are called intensive continuous local search methods. Given the potential of this type of local optimisation methods,

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Bharothu, Dr Jyothilal Nayak, Dr B. Madhu Kiran, Dr G. Kishor Babu, and B. N. V. Satish Kumar Kolla. "IEEE -30 Bus System Study with Memetic Differential Evolution Algorithm." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (2019): 86–96. http://dx.doi.org/10.5373/jardcs/v11i11/20193172.

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12

Wang, Longda, Xingcheng Wang, and Gang Liu. "An Improved Force Characteristic Curve Fitting Algorithm of Urban Rail Vehicles." Journal of Sensors 2022 (April 1, 2022): 1–13. http://dx.doi.org/10.1155/2022/9910982.

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In this paper, an improved force characteristic curve fitting memetic algorithm of urban rail vehicles is proposed for establishing precise train operation models. In order to improve the memetic algorithm global convergence, three strategies are adopted. In the improved memetic algorithm framework, an improved moth-flame optimization is used in global search; an improved simulated annealing is applied in local search; a new learning mechanism incorporated into reverse learning is adopted. Experimental simulation results under real-time data monitoring system show that the improved memetic alg

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Ammaruekarat, Paranya, and Phayung Meesad. "Multi-Objective Chaos Memetic Algorithm for DTLZ Problems." Advanced Materials Research 403-408 (November 2011): 3676–81. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3676.

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Based on Multi-Objective Memetic Algorithm (MOMA), a novel Multi-Objective Chaos Memetic Algorithm (MOCMA) is proposed . MOCMA is presented to keep population’s diversity, avoid local optimum and improve performance of Multi-Objective Memetic Algorithm. By virtue of the over-spread character of chaos sequence, it is used to generate chromosome to overcome redundancies. At the same time, searching space is enlarged by using sensitivity of chaos initial value. The comparisons of MOCMA with NSGAII in DTLZ problems suggest that MOCMA clearly outperforms in converging towards the true pareto front

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Lozano, Manuel, Francisco Herrera, Natalio Krasnogor, and Daniel Molina. "Real-Coded Memetic Algorithms with Crossover Hill-Climbing." Evolutionary Computation 12, no. 3 (2004): 273–302. http://dx.doi.org/10.1162/1063656041774983.

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This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to

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Engin, Orhan, and Batuhan Engin. "Hybrid flow shop with multiprocessor task scheduling based on earliness and tardiness penalties." Journal of Enterprise Information Management 31, no. 6 (2018): 925–36. http://dx.doi.org/10.1108/jeim-04-2017-0051.

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Purpose Hybrid flow shop with multiprocessor task (HFSMT) has received considerable attention in recent years. The purpose of this paper is to consider an HFSMT scheduling under the environment of a common time window. The window size and location are considered to be given parameters. The research deals with the criterion of total penalty cost minimization incurred by earliness and tardiness of jobs. In this research, a new memetic algorithm in which a global search algorithm is accompanied with the local search mechanism is developed to solve the HFSMT with jobs having a common time window.

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Kralev, Velin, and Radoslava Kraleva. "Combining Genetic Algorithm with Local Search Method in Solving Optimization Problems." Electronics 13, no. 20 (2024): 4126. http://dx.doi.org/10.3390/electronics13204126.

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This research is focused on evolutionary algorithms, with genetic and memetic algorithms discussed in more detail. A graph theory problem related to finding a minimal Hamiltonian cycle in a complete undirected graph (Travelling Salesman Problem—TSP) is considered. The implementations of two approximate algorithms for solving this problem, genetic and memetic, are presented. The main objective of this study is to determine the influence of the local search method versus the influence of the genetic crossover operator on the quality of the solutions generated by the memetic algorithm for the sam

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Yoon, Yourim, and Zong Woo Geem. "Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm." International Journal of Photoenergy 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/963562.

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This study proposes a memetic approach for optimally determining the parameter values of single-diode-equivalent solar cell model. The memetic algorithm, which combines metaheuristic and gradient-based techniques, has the merit of good performance in both global and local searches. First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, differential evolution, cuckoo search, least squares method, and pattern search; then their final solutions were used as initial vectors for generalized reduced gradient techniqu

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Kaweegitbundit, Parinya. "Comparison of Heuristic for Flow Shop Scheduling Problems with Sequence Dependent Setup Time." Advanced Materials Research 339 (September 2011): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.339.332.

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This paper considers flow shop scheduling problems with sequence dependent setup time. The makespan criterion has been considered. In this paper presented a comparison of three heuristics for solves this problem. The memetic algorithm, genetic algorithm and NEH heuristic have been compared. In the experimental, the result from memetic algorithm is maximum the best solution. Therefore, the MA heuristic outperforms other heuristic.

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Nuswantari, Ike Putri, and Mimiep Setyowati Madja. "Program untuk permasalahan multiple trip vehicle routing problem (MTVRP) menggunakan algoritma memetic pada proses pendistribusian." Jurnal MIPA dan Pembelajarannya 2, no. 12 (2023): 5. http://dx.doi.org/10.17977/um067v2i122022p5.

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In everyday life almost all problems require the help of mathematics, one of them on the transportation or on the distribution prosess. Multiple Trip Vehicle Routing Problem (MTVRP) is one of the problems related to the transportation or distribution prosess. Multiple Trip Vehicle Routing Problem (MTVRP) is defined as the problem of the Vehicle Routing Problem (VRP) with the expansion and the addition of multiple trips on the each vehicle when it distributes goods and the time window of customer service. One algorithm to solve the Multiple Trip Vehicle Routing Problem (MTVRP) is memetic algori

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Mahdi, Samir, and Brahim Nini. "Improved Memetic NSGA-II Using a Deep Neighborhood Search." International Journal of Applied Metaheuristic Computing 12, no. 4 (2021): 138–54. http://dx.doi.org/10.4018/ijamc.2021100108.

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Elitist non-sorted genetic algorithms as part of Pareto-based multi-objective evolutionary algorithms seems to be one of the most efficient algorithms for multi-objective optimization. However, it has some shortcomings, such as low convergence accuracy, uneven Pareto front distribution, and slow convergence. A number of review papers using memetic technique to improve NSGA-II have been published. Hence, it is imperative to improve memetic NSGA-II by increasing its solving accuracy. In this paper, an improved memetic NSGA-II, called deep memetic non-sorted genetic algorithm (DM-NSGA-II), is pro

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Godinho, Pedro, Luiz Moutinho, and Margherita Pagani. "A memetic algorithm for maximizing earned attention in social media." Journal of Modelling in Management 12, no. 3 (2017): 364–85. http://dx.doi.org/10.1108/jm2-10-2015-0078.

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Purpose The purpose of this study is to propose a measure for earned attention and a model and procedure for the maximization of earned attention by a company over a period of time. Design/methodology/approach Utility functions are used as the base of the earned attention measure. An evolutionary algorithm – a memetic algorithm – is applied to identify strategies that aim to maximize earned attention. Computational analysis is performed resorting to simulated data, and the memetic algorithm is assessed through the comparison with a standard steepest ascent heuristic. Findings The shape of the

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Zhang, Guohui, Jinghe Sun, Xixi Lu, and Haijun Zhang. "An improved memetic algorithm for the flexible job shop scheduling problem with transportation times." Measurement and Control 53, no. 7-8 (2020): 1518–28. http://dx.doi.org/10.1177/0020294020948094.

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In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divide

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Pu, Huangzhong, Ziyang Zhen, and Daobo Wang. "Modified shuffled frog leaping algorithm for optimization of UAV flight controller." International Journal of Intelligent Computing and Cybernetics 4, no. 1 (2011): 25–39. http://dx.doi.org/10.1108/17563781111115778.

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PurposeAttitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.Design/methodology/approachAmong the

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Tampubolon, Ferdinan Rinaldo, Rischa Devita, Sinta Marito Siagian, Samaria Chrisna HS, Switamy Angnitha Purba, and Anna Angela Sitinjak. "Penggunaan Memetic Algorithm Pada Penjadwalan Mesin untuk Meminimumkan Emisi Karbon." JURNAL PEMBELAJARAN DAN MATEMATIKA SIGMA (JPMS) 10, no. 1 (2024): 19–26. http://dx.doi.org/10.36987/jpms.v10i1.5121.

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Isu lingkungan merupakan topik penelitian yang sering dilakukan akhir-akhir ini, salah isu tersebut adalah adalah emisi karbon. Salah satu gas emisi karbon terbesar muncul dari tindakan-tindakan manusia seperti transportasi, pertambangan, aktifitas konstruksi dan industri. Dari sisi operasional, strategi penjadwalan terbukti secara signifikan mengurangi emisi karbon. Strategi ini dianggap lebih efektif dan rendah biaya. Flexible Job Shop adalah salah satu jenis penjadwalan mesin pada industri dan merupakan pengembangan dari Job Shop. Kebanyakan penelitian terkait Flexible Job Shop menggunakan

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Lin, Xianghong, Tingyu Ren, Jie Yang, and Xiangwen Wang. "Multi-objective cellular memetic algorithm." International Journal of Computing Science and Mathematics 15, no. 3 (2022): 213. http://dx.doi.org/10.1504/ijcsm.2022.124723.

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Wang, Xiangwen, Jie Yang, Tingyu Ren, and Xianghong Lin. "Multi-objective cellular memetic algorithm." International Journal of Computing Science and Mathematics 15, no. 3 (2022): 213. http://dx.doi.org/10.1504/ijcsm.2022.10049412.

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Sheng, Weiguo, Gareth Howells, Michael Fairhurst, and Farzin Deravi. "A Memetic Fingerprint Matching Algorithm." IEEE Transactions on Information Forensics and Security 2, no. 3 (2007): 402–12. http://dx.doi.org/10.1109/tifs.2007.902681.

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Zhang, Guanghui, Wenjing Ma, Keyi Xing, Lining Xing, and Kesheng Wang. "Quantum-Inspired Distributed Memetic Algorithm." Complex System Modeling and Simulation 2, no. 4 (2022): 334–53. http://dx.doi.org/10.23919/csms.2022.0021.

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Zhang, Liping, Xinyu Li, Long Wen, and Guohui Zhang. "An Efficient Memetic Algorithm for Dynamic Flexible Job Shop Scheduling with Random Job Arrivals." International Journal of Software Science and Computational Intelligence 5, no. 1 (2013): 63–77. http://dx.doi.org/10.4018/ijssci.2013010105.

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Much of the research on flexible job shop scheduling problem has ignored dynamic events in dynamic environment where there are complex constraints and a variety of unexpected disruptions. This paper proposes an efficient memetic algorithm to solve the flexible job shop scheduling problem with random job arrivals. Firstly, a periodic policy is presented to update the problem condition and generate the rescheduling point. Secondly, the efficient memetic algorithm with a new local search procedure is proposed to optimize the problem at each rescheduling point. Five kinds of neighborhood structure

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Zhou, Yu Yu, Yun Qing Rao, Guo Jun Zhang, and Chao Yong Zhang. "An Adaptive Memetic Algorithm for Packing Problems of Irregular Shapes." Advanced Materials Research 314-316 (August 2011): 1029–33. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.1029.

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Packing problem, which occurs frequently in sheet metal, clothing and furniture industry, cut product patterns from raw materials most efficiently and maximize material utilization. In this paper, an adaptive memetic algorithm is proposed to solve the problem of irregular shapes packed on the rectangular sheets. First, operators and parameters of evolution is researched, and second, local search method is proposed. Finally, this study compares benchmarks presented by other authors. The results show that the material utilization efficiency by using the adaptive memetic algorithm is higher compa

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Ulima Achva, RA Jihan, and Mohammad Fadhli. "Optimasi Efisiensi Antena Microstrip Circular Patch menggunakan Optimizer CST, Algoritma Memetika, dan Slot Rectangular pada WiFi 5 GHz." Jurnal RESISTOR (Rekayasa Sistem Komputer) 6, no. 1 (2023): 29–33. http://dx.doi.org/10.31598/jurnalresistor.v6i1.1401.

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Wireless communication is an important requirement in various applications, especially in WiFi networks. The efficiency of an antenna is a crucial aspect in designing wireless communication system and plays a significant role in ensuring strong and reliable signal transmission. This research belongs to comparison of the result of optimizing the efficiency of microstrip circular patch antennas for 5GHz WiFi frequencies using the optimizer in CST, memetic algorithm and rectangular slot. From the simulation result, the efficiency values of the microstrip circular patch antennas using different op

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Farah, Ihsen, Thi Nguyet Que Nguyen, Audrey Groh, Dominique Guenot, Pierre Jeannesson, and Cyril Gobinet. "Development of a memetic clustering algorithm for optimal spectral histology: application to FTIR images of normal human colon." Analyst 141, no. 11 (2016): 3296–304. http://dx.doi.org/10.1039/c5an02227d.

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Kumar, Sandeep, Vivek Kumar Sharma, and Rajani Kumari. "Memetic Search in Differential Evolution Algorithm." International Journal of Computer Applications 90, no. 6 (2014): 40–47. http://dx.doi.org/10.5120/15582-4406.

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QI, Yu-Tao, Fang LIU, Wei-Yuan CHANG, Xiao-Liang MA, and Li-Cheng JIAO. "Memetic Immune Algorithm for Multiobjective Optimization." Journal of Software 24, no. 7 (2014): 1529–44. http://dx.doi.org/10.3724/sp.j.1001.2013.04282.

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Korosec, Peter, Gregor Papa, and Vida Vukasinovic. "Production scheduling with a memetic algorithm." International Journal of Innovative Computing and Applications 2, no. 4 (2010): 244. http://dx.doi.org/10.1504/ijica.2010.036812.

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Tang, Maolin, and Xin Yao. "A Memetic Algorithm for VLSI Floorplanning." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37, no. 1 (2007): 62–69. http://dx.doi.org/10.1109/tsmcb.2006.883268.

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Tong, Guowei, Hongyan Chen, and Shi Liu. "Memetic reconstruction algorithm for the ECT." IET Science, Measurement & Technology 12, no. 7 (2018): 917–24. http://dx.doi.org/10.1049/iet-smt.2018.5241.

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Rezapoor Mirsaleh, M., and M. R. Meybodi. "A learning automata-based memetic algorithm." Genetic Programming and Evolvable Machines 16, no. 4 (2015): 399–453. http://dx.doi.org/10.1007/s10710-015-9241-9.

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Lü, Zhipeng, and Jin-Kao Hao. "A memetic algorithm for graph coloring." European Journal of Operational Research 203, no. 1 (2010): 241–50. http://dx.doi.org/10.1016/j.ejor.2009.07.016.

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Mashwani, Wali Khan, and Abdellah Salhi. "Multiobjective memetic algorithm based on decomposition." Applied Soft Computing 21 (August 2014): 221–43. http://dx.doi.org/10.1016/j.asoc.2014.03.007.

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Amen, AL-Khafaji. "Memetic Algorithm for Dynamic Optimization Problems." International Journal of Computer Applications 136, no. 3 (2016): 7–10. http://dx.doi.org/10.5120/ijca2016908393.

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Lin, Yung Chien. "A Memetic Algorithm for Mixed-Integer Optimization Problems." Applied Mechanics and Materials 284-287 (January 2013): 2970–74. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2970.

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Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. Th

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Peng, Lei, Yanyun Zhang, Guangming Dai, and Maocai Wang. "Memetic Differential Evolution with an Improved Contraction Criterion." Computational Intelligence and Neuroscience 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1395025.

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Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local se

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Zhang, Guo Hui. "Solving the Flexible Job Shop Scheduling Problem Based on Memetic Algorithm." Advanced Materials Research 544 (June 2012): 1–5. http://dx.doi.org/10.4028/www.scientific.net/amr.544.1.

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Flexible job shop scheduling problem (FJSP) is a well known NP-hard combinatorial optimization problem due to its very large search space and many constraint between jobs and machines. Evolutionary algorithms are the most widely used techniques in solving FJSP. Memetic algorithm is a hybrid evolutionary algorithm that combines the local search strategy and global search strategy. In this paper, an effective memetic algorithm is proposed to solve the FJSP. In the proposed algorithm, variable neighborhood search is adopted as local search algorithm. The neighborhood functions is generated by exc

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Kaweegitbundit, Parinya. "A Single Machine Scheduling Problem with Earliness and Tardiness Penalties Using Memetic Algorithm." Advanced Materials Research 314-316 (August 2011): 2353–57. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.2353.

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This paper considers single machine scheduling problem. The objective is to determine sum of earliness and tardiness cost has been minimized. The memetic algorithm is developed to solve this problem. To evaluate performance of memetic algorithm, the solution of propose method is compare with the optimal solution. The results show that the average percentage deviation is less than 10. Here, the computational time required by the MA is significantly less than the time required by the optimal solution method. This result is more emphasized as the problem size is getting larger.

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Huy, Nguyen Quang, Ong Yew Soon, Lim Meng Hiot, and Natalio Krasnogor. "Adaptive Cellular Memetic Algorithms." Evolutionary Computation 17, no. 2 (2009): 231–56. http://dx.doi.org/10.1162/evco.2009.17.2.231.

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A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperfo

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Zhang, Zhiqiang, Zhongwen Li, Xiaobing Qiao, and Weijun Wang. "An Efficient Memetic Algorithm for the Minimum Load Coloring Problem." Mathematics 7, no. 5 (2019): 475. http://dx.doi.org/10.3390/math7050475.

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Given a graph G with n vertices and l edges, the load distribution of a coloring q: V → {red, blue} is defined as dq = (rq, bq), in which rq is the number of edges with at least one end-vertex colored red and bq is the number of edges with at least one end-vertex colored blue. The minimum load coloring problem (MLCP) is to find a coloring q such that the maximum load, lq = 1/l × max{rq, bq}, is minimized. This problem has been proved to be NP-complete. This paper proposes a memetic algorithm for MLCP based on an improved K-OPT local search and an evolutionary operation. Furthermore, a data spl

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Zhang, Guo Hui. "Using the Memetic Algorithm for Multi Objective Job Shop Scheduling Problems." Advanced Materials Research 544 (June 2012): 245–50. http://dx.doi.org/10.4028/www.scientific.net/amr.544.245.

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The multi objective job shop scheduling problem is well known as one of the most complex optimization problems due to its very large search space and many constraint between machines and jobs. In this paper, an evolutionary approach of the memetic algorithm is used to solve the multi objective job shop scheduling problems. Memetic algorithm is a hybrid evolutionary algorithm that combines the global search strategy and local search strategy. The objectives of minimizing makespan and mean flow time are considered while satisfying a number of hard constraints. The computational results demonstra

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Yoon, Yourim, and Yong-Hyuk Kim. "A Memetic Lagrangian Heuristic for the 0-1 Multidimensional Knapsack Problem." Discrete Dynamics in Nature and Society 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/474852.

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We present a new evolutionary algorithm to solve the 0-1 multidimensional knapsack problem. We tackle the problem using duality concept, differently from traditional approaches. Our method is based on Lagrangian relaxation. Lagrange multipliers transform the problem, keeping the optimality as well as decreasing the complexity. However, it is not easy to find Lagrange multipliers nearest to the capacity constraints of the problem. Through empirical investigation of Lagrangian space, we can see the potentiality of using a memetic algorithm. So we use a memetic algorithm to find the optimal Lagra

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Shereen, S. Sadiq, Mohsin Abdulazeez Adnan, and Haron Habibollah. "Solving multi-objective master production schedule problem using memetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 2 (2020): 938–45. https://doi.org/10.11591/ijeecs.v18.i2.pp938-945.

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A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to perform optimization on production planning problems based on, for instance, linear and non-linear programming, dynamic-lot sizing and meta-heuristics. In particular, several meta- heuristics have been successfully used to solve MPS problems such as genetic algorithms (GA) and simulated annealing (S

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