Nature-inspired approach: An enhanced moth swarm algorithm for global optimization

Abstract The moth swarm algorithm (MSA) is a recent swarm intelligence optimization algorithm, but its convergence precision and ability can be limited in some applications. To enhance the MSA’s exploration abilities, an enhanced MSA called the elite opposition-based MSA (EOMSA) is proposed. For the EOMSA, an elite opposition-based strategy is used to enhance the diversity of the population and its exploration ability. The EOMSA was validated using 23 benchmark functions and three structure engineering design problems. The results show that the EOMSA can find a more accurate solution than other population-based algorithms, and it also has a fast convergence speed and high degree of stability.

[1]  Ali Kaveh,et al.  Water Evaporation Optimization , 2016 .

[2]  R. Mantegna,et al.  Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[4]  Al-Attar Ali Mohamed,et al.  Multi-objective states of matter search algorithm for TCSC-based smart controller design , 2016 .

[5]  P S Callahan.  Moth and candle: the candle flame as a sexual mimic of the coded infrared wavelengths from a moth sex scent (pheromone). , 1977, Applied optics.

[6]  Al-Attar Ali Mohamed,et al.  Optimal power flow using moth swarm algorithm , 2017 .

[7]  Li Chen,et al.  TAGUCHI-AIDED SEARCH METHOD FOR DESIGN OPTIMIZATION OF ENGINEERING SYSTEMS , 1998 .

[8]  R Menzel,et al.  Associative learning of plant odorants activating the same or different receptor neurones in the moth Heliothis virescens , 2005, Journal of Experimental Biology.

[9]  Siamak Talatahari,et al.  An improved ant colony optimization for constrained engineering design problems , 2010 .

[10]  Fevrier Valdez,et al.  Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition , 2015, Expert Syst. Appl..

[11]  Ragab A. El-Sehiemy,et al.  Optimal power flow using an Improved Colliding Bodies Optimization algorithm , 2016, Appl. Soft Comput..

[12]  Myron P. Zalucki,et al.  Learning, odour preference and flower foraging in moths , 2004, Journal of Experimental Biology.

[13]  Belkacem Mahdad,et al.  Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm , 2016, Appl. Soft Comput..

[14]  A. Kaveh,et al.  A new meta-heuristic method: Ray Optimization , 2012 .

[15]  Ling Wang,et al.  An effective co-evolutionary differential evolution for constrained optimization , 2007, Appl. Math. Comput..

[16]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[17]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[18]  Patricia Melin,et al.  Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems , 2017, Appl. Soft Comput..

[19]  Harish Garg,et al.  A hybrid PSO-GA algorithm for constrained optimization problems , 2016, Appl. Math. Comput..

[20]  Tsung-Jung Hsieh,et al.  A bacterial gene recombination algorithm for solving constrained optimization problems , 2014, Appl. Math. Comput..

[21]  Oscar Castillo,et al.  Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions , 2017, Algorithms.

[22]  Oscar Castillo,et al.  A generalized type-2 fuzzy logic approach for dynamic parameter adaptation in bee colony optimization applied to fuzzy controller design , 2017, Inf. Sci..

[23]  Ehsan Amiri,et al.  Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm , 2016, Appl. Soft Comput..

[24]  Varun Punnathanam,et al.  Yin-Yang-pair Optimization: A novel lightweight optimization algorithm , 2016, Eng. Appl. Artif. Intell..

[25]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[26]  Jonathan Bennie,et al.  The ecological impacts of nighttime light pollution: a mechanistic appraisal , 2013, Biological reviews of the Cambridge Philosophical Society.

[27]  Yong Wang,et al.  Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization , 2010, Appl. Soft Comput..

[28]  K. D. Frank,et al.  Impact of outdoor lighting on moths: an assessment , 1988 .

[29]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

[30]  Erwie Zahara,et al.  Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems , 2009, Expert Syst. Appl..

[31]  Adil Baykasoglu,et al.  Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems - Part 1: Unconstrained optimization , 2015, Appl. Soft Comput..

[32]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[33]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[34]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[35]  Ardeshir Bahreininejad,et al.  Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems , 2013, Appl. Soft Comput..

[36]  Oscar Castillo,et al.  A fuzzy hierarchical operator in the grey wolf optimizer algorithm , 2017, Appl. Soft Comput..

[37]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[38]  Oscar Castillo,et al.  A new optimization meta-heuristic algorithm based on self-defense mechanism of the plants with three reproduction operators , 2018, Soft Comput..

[39]  Kalyanmoy Deb,et al.  Optimal design of a welded beam via genetic algorithms , 1991 .

[40]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[41]  Ashish Kumar Bhandari,et al.  Optimal sub-band adaptive thresholding based edge preserved satellite image denoising using adaptive differential evolution algorithm , 2016, Neurocomputing.

[42]  Anderson,et al.  Behavioural analysis of olfactory conditioning in the moth spodoptera littoralis (Boisd.) (Lepidoptera: noctuidae) , 1997, The Journal of experimental biology.

[43]  Adil Baykasoglu,et al.  Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems - Part 2: Constrained optimization , 2015, Appl. Soft Comput..

[44]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[45]  M. Hammer,et al.  Functional Organization of Appetitive Learning and Memory in a Generalist Pollinator, the Honey Bee , 1993 .

[46]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[47]  Ali Sadollah,et al.  Water cycle algorithm for solving constrained multi-objective optimization problems , 2015, Appl. Soft Comput..

[48]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

[49]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..