Probability collectives hybridised with differential evolution for global optimisation

Abstract

Probability collectives (PC) is a recent agent-based search framework for function optimisation through optimising parameters of a collection of probability distributions. Differential evolution (DE) is a successful metaheuristic method particularly for real-parameter global optimisation. This paper presents a hybrid computational model based on a modified PC and DE algorithms for the purpose of improved solutions for real-valued optimisation problems. In the proposed model, PC performs a first phase local search and explores promising search areas through updating parameters of probability distributions over the solution space while DE uses the extracted PC-based knowledge to guide its search with adaptive heuristics. A novel distance-based adaptive mutation scheme is designed within DE to guide the search towards better regions of the solution space. Experimental results reveal that the proposed hybrid algorithm is able to integrate the PC's collective learning methodology and DE's adaptive search strategy effectively to generate improved solutions for difficult problems.