Metaheuristic Algorithms: Guidelines for Implementation

Ashkan Memari, Robiah Ahmad, Abd. Rahman Abdul Rahim


This paper presents a quick review of the basic concepts and essential steps for implementing of metaheuristic algorithms. It can be therefore used as a roadmap to shed light on solving an optimization problem using a metaheuristic algorithm. We provide a brief review of the topics, including general concepts for metaheuristics, the need to design metaheuristics, the need for further improvement of metaheuristics, parameters tuning and performance assessment of metaheuristic algorithms. Finally, the paper ends with a guideline framework which aims to assist new researchers for solving optimization problems via metaheuristics.      


Metaheuristic Algorithms, Optimization, Literature Review, Performance Assessment, Guidelines for Implementation

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Blum, C., Blesa, M.J., Aguilera, A. Roli, M. (2008), Hybrid Metaheuristics-An Emerging Approach to Optimization, Studies in Computational Intelligence, volume 114, Springer-Verlag, Berlin, Germany.

Blum, C., Puchinger, J., Raidl, G. R. and Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing. 11 (6): 4135-4151.

Boussaïd, I., Lepagnot, J. and Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences. 237: 82-117.

Chinneck, J. W. (2004). Practical optimization: a gentle introduction. Electronic document: http://www. sce. carleton. ca/faculty/chinneck/po. html.

Coello, C. C., Lamont, G. B. and Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems. Springer Science & Business Media.

Deb, K. and Jain, S. (2002). Running performance metrics for evolutionary ourth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'02),(Singapore), Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'02), Singapore.

Yang, X.-S. (2010). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons.

Glover, F. and Kochenberger, G. A. (2003). Handbook of metaheuristics. Springer.

Talbi, E.-G. (2009). Metaheuristics: from design to implementation. John Wiley & Sons.

Lozano, M. and García-Martínez, C. (2010). Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report. Computers & Operations Research. 37 (3): 481-497.

Knowles, J. D. and Corne, D. W. (2000). Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary computation. 8 (2): 149-172.

Hansen, M. P. and Jaszkiewicz, A. (1998). Evaluating the quality of approximations to the non-dominated set. IMM, Department of Mathematical Modelling, Technical Universityof Denmark.

Van Veldhuizen, D. and Lamont, G. B. (2000). On measuring multiobjective evolutionary algorithm performance. Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, IEEE.

Zitzler, E., Deb, K. and Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary computation. 8 (2): 173-195.

Zitzler, E., Laumanns, M., Thiele, L., Fonseca, C. M. and da Fonseca, V. G. (2002). Why Quality Assessment Of Multiobjective Optimizers Is Difficult. GECCO.


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