Abstract
This paper proposes a multi-population adaptive version of inflationary differential evolution algorithm. Inflationary differential evolution algorithm (IDEA) combines basic differential evolution (DE) with some of the restart and local search mechanisms of Monotonic Basin Hopping (MBH). In the adaptive version presented in this paper, the DE parameters { CR} and F are automatically adapted together with the size of the local restart bubble and the number of local restarts of MBH. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The algorithm has been extensively tested over more than fifty test functions from the competitions of the Congress on Evolutionary Computation (CEC), CEC 2005, CEC 2011 and CEC 2014, and compared against all the algorithms participating in those competitions. For each test function, the paper reports best, worst, median, mean and standard deviation values of the best minimum found by the algorithm. Comparisons with other algorithms participating in the CEC competitions are presented in terms of relative ranking, Wilcoxon tests and success rates. For completeness, the paper presents also the single population adaptive IDEA, that can adapt only textit{CR} and F, and shows that this simpler version can outperform the multi-population one if the radius of the restart bubble and the number of restarts are properly chosen.
Highlights
Differential evolution (DE), proposed by Price et al (2006), is a well-known population-based evolutionary algorithm for solving global optimisation problems over continuous spaces
This paper presented MP-AIDEA, an adaptive version of inflationary differential evolution which automatically adapts the two key parameters of differential evolution, CR, F, the size of the restart bubble δlocal and the number of local restarts nLR
MP-AIDEA was tested on a total of 51 problems, taken from three Congress on Evolutionary Computation (CEC) competitions, grouped in three test sets and compared against 53 algorithms that participated in those three competitions
Summary
Differential evolution (DE), proposed by Price et al (2006), is a well-known population-based evolutionary algorithm for solving global optimisation problems over continuous spaces. IDEA was shown to give better results than a simple DE, but its performance is dependent upon the parameters controlling both the DE and MBH heuristics (Vasile et al 2011) These parameters are the crossover probability CR, the differential weight F, the radius of the local restart bubble δlocal and the number of local restarts nLR, whose best settings are problem dependent. This paper provides a more detailed explanation of all the mechanisms and heuristics inside MP-AIDEA; it presents an extensive empirical assessment of its performance, using several metrics in addition to the relative ranking As part of this extensive performance evaluation, we compare MP-AIDEA against a number of other algorithms and a single population version of MP-AIDEA with no adaptive local restart.
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