ارائه یک الگوریتم ترکیبی با استفاده از الگوریتم کرم شب‌تاب، الگوریتم ژنتیک و جست‌وجوی محلی

سلیمی, جواد; گلی بیدگلی, سلمان

doi:10.22052/8.1.14

ارائه یک الگوریتم ترکیبی با استفاده از الگوریتم کرم شب‌تاب، الگوریتم ژنتیک و جست‌وجوی محلی

نویسندگان

دانشگاه کاشان

10.22052/8.1.14

چکیده

در این مقاله، یک الگوریتم ترکیبی چندهدفه ارائه شده است که ویژگی‌های دو الگوریتم ژنتیک و کرم‌ شب‌تاب را ترکیب می‌کند. این الگوریتم با مجموعه‌ای از کرم‌های شب‌تاب که در فضای مسئله به‌صورت تصادفی پخش می‌‌شوند، شروع به کار می‌‌کند و این ذرات طی‌ مراحل تکامل، به جواب بهینه مسئله همگرا می‌‌شوند. سپس یک طرح جست‌وجوی محلی به‌عنوان روشی برای جست‌وجوی همسایگی به‌منظور بهبود کیفیت جواب‌ها ارائه و پیاده‌سازی شده است. این بخش از الگوریتم برای جست‌وجوی نواحی کم‌جمعیت، برای یافتن جواب‌های غالب استفاده می‌شود. برای بهبود الگوریتم تغییراتی در معیار تعیین بهترین بهینه سراسری به‌ازای هر کرم شب‌تاب و همچنین بهترین بهینه محلی اعمال شده است. استفاده از این روش موجب شده یکنواختیِ منحنیِ پرتو بیشتر شود. نتایج آزمایشگاهی روش ارائه شده بر روی برخی از توابع محک نشان می‌دهد که به‌کارگیری این روش موجب کاهش خطا شده است. الگوریتم پیشنهادی بر اساس یک الگوریتم پایه توسعه داده شده است.

کلیدواژه‌ها

20.1001.1.23223707.1398.8.1.2.8

عنوان مقاله [English]

A Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms

نویسندگان [English]

Javad Salimisartaghti
گلی Goli

چکیده [English]

In this paper, a hybrid multi-objective algorithm consisting of features of genetic and firefly algorithms is presented. The algorithm starts with a set of fireflies (particles) that are randomly distributed in the solution space; these particles converge to the optimal solution of the problem during the evolutionary stages. Then, a local search plan is presented and implemented for searching solution neighbors to improve the quality of global solutions. This part of the algorithm is used to search sparsely populated areas for finding the dominant solutions. To improve the algorithm, for each firefly some changes have been made on the criteria of determining the global optimal solution and doing local optimal solution; this leads to more uniformity of the Pareto curve and error reduction, as the experimental results show. The proposed algorithm is an extension of a basic algorithm.

کلیدواژه‌ها [English]

Firefly algorithm
Genetics algorithm
Local Search
multi-objective algorithm
Hybrid algorithm

مراجع

[1] C. A. C. Coello, G. B. Lamont, and D. A. Van Veldhuizen, Evolutionary algorithms for solving multi-objective problems. Springer, 2007. [2] K. Miettinen, Nonlinear multiobjective optimization. Springer Science & Business Media, 2012. [3] A. Trivedi, D. Srinivasan, K. Sanyal, and A. Ghosh, "A survey of multiobjective evolutionary algorithms based on decomposition," IEEE Transactions on Evolutionary Computation, vol. 21, no. 3, pp. 440-462, 2016. [4] C. R. Reeves, "Genetic algorithms," in Handbook of metaheuristics: Springer, 2010, pp. 109-139. [5] X.-S. Yang, Nature-inspired metaheuristic algorithms. Luniver press, 2010. [6] J. Brest and M. S. Maučec, "Self-adaptive differential evolution algorithm using population size reduction and three strategies," Soft Computing, vol. 15, no. 11, pp. 2157-2174, 2011. [7] S. Tiwari, G. Fadel, P. Koch, and K. Deb, "Performance assessment of the hybrid archive-based micro genetic algorithm (AMGA) on the CEC09 test problems," in 2009 IEEE Congress on Evolutionary Computation, 2009, pp. 1935-1942: IEEE. [8] B.-Y. Qu and P. N. Suganthan, "Multi-objective evolutionary programming without non-domination sorting is up to twenty times faster," in 2009 IEEE Congress on Evolutionary Computation, 2009, pp. 2934-2939: IEEE. [9] M. Ali, P. Siarry, and M. Pant, "An efficient differential evolution based algorithm for solving multi-objective optimization problems," European journal of operational research, vol. 217, no. 2, pp. 404-416, 2012. [10] S. Noghanian, A. Sabouni, T. Desell, and A. Ashtari, "Global optimization: Differential evolution, genetic algorithms, particle swarm, and hybrid methods," in Microwave Tomography: Springer, 2014, pp. 39-61. [11] M. J. Mahmoodabadi, A. A. Safaie, A. Bagheri, and N. Nariman-Zadeh, "A novel combination of Particle Swarm Optimization and Genetic Algorithm for Pareto optimal design of a five-degree of freedom vehicle vibration model," Applied Soft Computing, vol. 13, no. 5, pp. 2577-2591, 2013. [12] X.-S. Yang, "Firefly algorithm," Nature-inspired metaheuristic algorithms, vol. 20, pp. 79-90, 2008. [13] X.-S. Yang, Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons, 2010. [14] X.-S. Yang, "Firefly algorithms for multimodal optimization," in International symposium on stochastic algorithms, 2009, pp. 169-178: Springer. [15] M.-H. Horng and T.-W. Jiang, "The codebook design of image vector quantization based on the firefly algorithm," in International Conference on Computational Collective Intelligence, 2010, pp. 438-447: Springer. [16] T. Apostolopoulos and A. Vlachos, "Application of the firefly algorithm for solving the economic emissions load dispatch problem," International journal of combinatorics, vol. 2011, 2010. [17] X.-S. Yang, "Multiobjective firefly algorithm for continuous optimization," Engineering with computers, vol. 29, no. 2, pp. 175-184, 2013. [18] H. Wang et al., "A hybrid multi-objective firefly algorithm for big data optimization," Applied Soft Computing, vol. 69, pp. 806-815, 2018. [19] B. Amiri, L. Hossain, J. W. Crawford, and R. T. Wigand, "Community detection in complex networks: Multi–objective enhanced firefly algorithm," Knowledge-Based Systems, vol. 46, pp. 1-11, 2013. [20] A. Mousa, M. El-Shorbagy, and W. F. Abd-El-Wahed, "Local search based hybrid particle swarm optimization algorithm for multiobjective optimization," Swarm and Evolutionary Computation, vol. 3, pp. 1-14, 2012.