A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design

As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant.Thus,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of SMO.Moreover,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values.Also,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time.Furthermore,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization problems.The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems.Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.

On the analysis of ant colony optimization for the maximum independent set problem

1 Introduction Inspired by natural evolution and biological behavior,researchers have developed many successful bio-inspired algorithms.Ant colony optimization(ACO)is one of the most successful bio-inspired computing methods for complex optimization problems.In contrast to the wide range of applications,the theoretical understanding of this kind of algorithms lagged far behind[1].Therefore,it is desirable and necessary to improve the theoretical foundation of the algorithm in order to have a better understanding of the execution mechanism of the algorithm and guide the algorithm design.Many researches are devoted to understanding the working principles of bio-inspired algorithms,and try to bridge the gap between theoretical research and practical applications of the algorithms.Many encouraging results have been obtained[2].