Ageist Spider Monkey Optimization algorithm

Abstract Swarm Intelligence (SI) is quite popular in the field of numerical optimization and has enormous scope for research. A number of algorithms based on decentralized and self-organized swarm behavior of natural as well as artificial systems have been proposed and developed in last few years. Spider Monkey Optimization (SMO) algorithm, inspired by the intelligent behavior of spider monkeys, is one such recently proposed algorithm. The algorithm along with some of its variants has proved to be very successful and efficient. A spider monkey group consists of members from every age group. The agility and swiftness of the spider monkeys differ on the basis of their age groups. This paper proposes a new variant of SMO algorithm termed as Ageist Spider Monkey Optimization (ASMO) algorithm which seems more practical in biological terms and works on the basis of age difference present in spider monkey population. Experiments on different benchmark functions with different parameters and settings have been carried out and the variant with the best suited settings is proposed. This variant of SMO has enhanced the performance of its original version. Also, ASMO has performed better in comparison to some of the recent advanced algorithms.

[1]  Pandian Vasant,et al.  Swarm based mean-variance mapping optimization (MVMOs) for economic dispatch problem with valve — Point effects , 2014, 2014 IEEE International Conference on Industrial Engineering and Engineering Management.

[2]  Ming Yang,et al.  Differential Evolution With Auto-Enhanced Population Diversity , 2015, IEEE Transactions on Cybernetics.

[3]  Lingling Huang,et al.  Artificial Bee Colony Algorithm Based on Information Learning , 2015, IEEE Transactions on Cybernetics.

[4]  Nor Ashidi Mat Isa,et al.  Adaptive division of labor particle swarm optimization , 2015, Expert Syst. Appl..

[5]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[6]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[7]  Luca Maria Gambardella,et al.  A survey on metaheuristics for stochastic combinatorial optimization , 2009, Natural Computing.

[8]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[9]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[10]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[11]  Xiaodong Li,et al.  Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale , 2009 .

[12]  Narasimhan Sundararajan,et al.  Self regulating particle swarm optimization algorithm , 2015, Inf. Sci..

[13]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[14]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[15]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[16]  Xiang Yu,et al.  Enhanced comprehensive learning particle swarm optimization , 2014, Appl. Math. Comput..

[17]  Narasimhan Sundararajan,et al.  Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems , 2016, Inf. Sci..

[18]  Jaya Sil,et al.  Levy distributed parameter control in differential evolution for numerical optimization , 2015, Natural Computing.

[19]  Lingling Huang,et al.  Enhancing artificial bee colony algorithm using more information-based search equations , 2014, Inf. Sci..

[20]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[21]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[22]  Eisuke Kita,et al.  Search performance improvement of Particle Swarm Optimization by second best particle information , 2014, Appl. Math. Comput..

[23]  Vivek Kumar Sharma,et al.  Modified Position Update in Spider Monkey Optimization Algorithm , 2014 .

[24]  Harish Sharma,et al.  Spider Monkey Optimization algorithm for numerical optimization , 2014, Memetic Computing.

[25]  Michel Gendreau,et al.  Metaheuristics in Combinatorial Optimization , 2022 .

[26]  Zuren Feng,et al.  A Scatter Learning Particle Swarm Optimization Algorithm for Multimodal Problems , 2014, IEEE Transactions on Cybernetics.

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

[28]  Saeed Tavakoli,et al.  An intelligent global harmony search approach to continuous optimization problems , 2014, Appl. Math. Comput..

[29]  Kenneth Sörensen,et al.  Metaheuristics - the metaphor exposed , 2015, Int. Trans. Oper. Res..

[30]  Yaochu Jin,et al.  A social learning particle swarm optimization algorithm for scalable optimization , 2015, Inf. Sci..

[31]  Jun Zhang,et al.  Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems , 2015, Inf. Sci..

[32]  Jing Wang,et al.  Swarm Intelligence in Cellular Robotic Systems , 1993 .

[33]  Kusum Deep,et al.  Tournament Selection Based Probability Scheme in Spider Monkey Optimization Algorithm , 2015, ICHSA.

[34]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

[35]  Witold Pedrycz,et al.  Superior solution guided particle swarm optimization combined with local search techniques , 2014, Expert Syst. Appl..