A chaos wolf optimization algorithm with self-adaptive variable step-size

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimi...

[1]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[2]  Teresa Wu,et al.  An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods , 2013, IEEE Transactions on Evolutionary Computation.

[3]  Renbin Xiao,et al.  Modelling for combat task allocation problem of aerial swarm and its solution using wolf pack algorithm , 2016 .

[4]  Rafael Stubs Parpinelli,et al.  A comparison of swarm intelligence algorithms for structural engineering optimization , 2012 .

[5]  Kyungsook Han,et al.  Bio-Inspired Computing and Applications , 2011, Lecture Notes in Computer Science.

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

[7]  Zheng Li,et al.  Expert Systems With Applications , 2022 .

[8]  Husheng Wu,et al.  An oppositional wolf pack algorithm for Parameter identification of the chaotic systems , 2016 .

[9]  Xin Zhang,et al.  Shift based adaptive differential evolution for PID controller designs using swarm intelligence algorithm , 2017, Cluster Computing.

[10]  Hamidreza Modares,et al.  System Identification and Control using Adaptive Particle Swarm Optimization , 2011 .

[11]  P. Pardalos,et al.  Recent developments and trends in global optimization , 2000 .

[12]  Husheng Wu,et al.  Wolf Pack Algorithm for Unconstrained Global Optimization , 2014 .

[13]  Debasish Ghose,et al.  Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions , 2009, Swarm Intelligence.

[14]  Xiaohui Shen,et al.  Precision time synchronization control method for smart grid based on wolf colony algorithm , 2016 .

[15]  Cheng Yongbo,et al.  Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm , 2017 .

[16]  Richard J. Duro,et al.  Evolutionary algorithm characterization in real parameter optimization problems , 2013, Appl. Soft Comput..

[17]  Qin Tang,et al.  Swarm Intelligence: Based Cooperation Optimization of Multi-Modal Functions , 2013, Cognitive Computation.

[18]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[19]  Bin Cao,et al.  Wolf-Pack Algorithm for Business Process Model Syntactic and Semantic Structure Verification in the Workflow Management Environment , 2010, 2010 IEEE Asia-Pacific Services Computing Conference.

[20]  Erik Cuevas,et al.  An Algorithm for Global Optimization Inspired by Collective Animal Behavior , 2012 .

[21]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[22]  M. J. Mahjoob,et al.  A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search , 2010, Comput. Math. Appl..

[23]  Juan A. Lazzús,et al.  Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm , 2016 .