A modified differential evolution algorithm for unconstrained optimization problems

• Published in: Neurocomputing (Amsterdam) Vol. 120; pp. 469 - 481
• Main Authors: Zou, Dexuan, Wu, Jianhua, Gao, Liqun, Li, Steven
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 23.11.2013; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Central solution; Computer science; control theory; systems; Exact sciences and technology; External archive; Gauss distribution; Mathematical programming; Modified differential evolution algorithm; Mutation; Operational research and scientific management; Operational research. Management science; Theoretical computing; Uniform distribution; External archive; Gauss distribution; Uniform distribution; Mutation; Modified differential evolution algorithm; Central solution; Archive; Unconstrained optimization; Evolutionary algorithm; Diversity; Scale factor; Random distribution; Global solution; Value function; Experimental result; Gaussian process; Randomness; Problem solving; Objective function; Mathematical programming
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2013.04.036

A modified differential evolution algorithm (MDE) is proposed to solve unconstrained optimization problems in this paper. Gauss distribution and uniform distribution have one thing in common, that is randomness or indeterminateness. Due to this characteristic, MDE employs both distributions to adjust scale factor and crossover rate, which is useful to increase the diversity of the entire population. To guarantee the quality of the swarm, MDE uses an external archive, and some solutions of high quality in this external archive can be selected for candidate solutions. MDE adopts two common mutation strategies to produce new solutions, and the information of global best solution is more likely to be utilized for the mutation during late evolution process, which is beneficial to improving the convergence of the proposed algorithm. In addition, a central solution is generated in terms of all the other candidate solutions, and it can provide a potential searing direction. Experimental results show that MDE algorithm can yield better objective function values than the other six DE algorithms for some unconstrained optimization problems, thus it is an efficient alternative on solving unconstrained optimization problems.