Abstract
In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/discrete meme representation and a probabilistic co-evolving PSO scheme for discrete, continuous, or hybrid spaces. The coevolving memetic PSO evolves both the solutions and their associated memes, i.e. the local search operators. The proposed CoMPSO approach has been experimented on a standard suite of numerical optimization benchmark problems. Preliminary experimental results show that CoMPSO is competitive with respect to standard PSO and other memetic PSO schemes in literature, and its a promising starting point for further research in adaptive PSO local search operators.
Highlights
Memetic algorithms (MAs) are a class of hybrid evolutionary algorithms (EAs) and have been widely applied to many complex optimization problems [1], such as scheduling problems [2,3,4], combinatorial optimization problems [5,6,7], multi-objective optimization problems [8,9], and multimodal optimization problems [10,11]
Population-based evolutionary approaches, such as genetic algorithms (GAs) or particle swarm optimization (PSO) [12], are able to detect in a fast way the main regions of attraction, while their local search abilities represent a major drawback [13] from the point of view of solution accuracy and of the convergence behaviour, especially when applied to multimodal problems
Many GA-based MAs and other EA approaches employing meme evolution have been proposed in the literature [13,19], the PSO memetic algorithms proposed so far fail to employ meme evolution, probably because of the difficulty of designing the descriptors of the local search operators, which are usually statically tailored to the problem, or the class of problems, at hand [7,8,10,11,20]
Summary
Memetic algorithms (MAs) are a class of hybrid evolutionary algorithms (EAs) and have been widely applied to many complex optimization problems [1], such as scheduling problems [2,3,4], combinatorial optimization problems [5,6,7], multi-objective optimization problems [8,9], and multimodal optimization problems [10,11]. Many GA-based MAs and other EA approaches employing meme evolution have been proposed in the literature [13,19], the PSO memetic algorithms proposed so far fail to employ meme evolution, probably because of the difficulty of designing the descriptors of the local search operators, which are usually statically tailored to the problem, or the class of problems, at hand [7,8,10,11,20] They exhibit, at most, only some form of temporary adaptation that is not preserved or transmitted through the generations as meta-Lamarkian memes [21].
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