Adaptive multi-population inflationary differential evolution

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 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. IntroductionDifferential evolution (DE), proposed by Price et al. (2006), is a well-known population-based evolutionary algorithm for solving global optimisation problems over continuous spaces. Literature indicates that DE exhibits very good performance over a wide variety of optimisation problems (Das and Suganthan 2011). However, although being a very efficient optimiser, its local search ability has long been questioned and work has been done to improve its local con-Communicated by V. Loia.

Preliminary spacecraft design by means of Structured-Chromosome Genetic Algorithms

“…MP-AIDEA [5], [7] is a population-based memetic algorithm for continuous optimisation, It was developed in order to improve the performance of existing Differential Evolution (DE) approaches [24]. The first proposed version, Inflationary Differential Evolution Algorithm (IDEA) [26], hybridised DE with the restarting procedure of the Monotonic Basin Hopping (MBH) algorithm.…”

“…AIDEA is then able to automatically adapt crossover probability (CR) and differential weight (F). The final version of the algorithm, MP-AIDEA [7], evolves multiple populations and allows for different DE strategies. Besides CR and F it also automatically adapts the maximum number of local restart (ius) and the bubble's dimention for the local restart (ρ local ).…”

“…Two models for the space system, with different order of complexity, have been implemented. To validate the proposed methodology, the performance of SCGA has been compared with the ones obtained with an established optimiser in the field, namely Multi-Population Adaptive Inflationary Differential Evolution Algorithm [5], [7].…”

A Memetic Approach to the Solution of Constrained Min-Max Problems

“…The optimiser used for both optimisation and restoration loops is M P -AIDEA [28]. M P -AIDEA has been used with one and three populations.…”

Modeling supersonic heated jet noise at fixedjetMach number using an asymptotic approach for the acoustic analogy Green’s function and an optimized turbulence model

“…Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA, [20] & [21]) is a populationbased evolutionary algorithm for solving single-objective global optimisation problems over continuous spaces. It combines adaptive Differential Evolution (DE) with the restarting procedure of Monotonic Basin Hopping (MBH) ( [22] & [23]).…”