2 research outputs found
Multi-Layer Competitive-Cooperative Framework for Performance Enhancement of Differential Evolution
Differential Evolution (DE) is recognized as one of the most powerful
optimizers in the evolutionary algorithm (EA) family. Many DE variants were
proposed in recent years, but significant differences in performances between
them are hardly observed. Therefore, this paper suggests a multi-layer
competitive-cooperative (MLCC) framework to facilitate the competition and
cooperation of multiple DEs, which in turns, achieve a significant performance
improvement. Unlike other multi-method strategies which adopt a
multi-population based structure, with individuals only evolving in their
corresponding subpopulations, MLCC implements a parallel structure with the
entire population simultaneously monitored by multiple DEs assigned to their
corresponding layers. An individual can store, utilize and update its evolution
information in different layers based on an individual preference based layer
selecting (IPLS) mechanism and a computational resource allocation bias (RAB)
mechanism. In IPLS, individuals connect to only one favorite layer. While in
RAB, high-quality solutions are evolved by considering all the layers. Thus DEs
associated in the layers work in a competitive and cooperative manner. The
proposed MLCC framework has been implemented on several highly competitive DEs.
Experimental studies show that the MLCC variants significantly outperform the
baseline DEs as well as several state-of-the-art and up-to-date DEs on CEC
benchmark functions
Selective-Candidate Framework with Similarity Selection Rule for Evolutionary Optimization
Achieving better exploitation and exploration capabilities (EEC) have always
been an important yet challenging issue in the design of evolutionary
optimization algorithm (EOA). The difficulties lie in obtaining a good balance
in EEC, which is determined cooperatively by operations and parameters in an
EOA. When deficiencies in exploitation or exploration are observed, most
existing works consider a piecemeal approach, either by designing new
operations or by altering the parameters. Unfortunately, when different
situations are encountered, these proposals may fail to be the winner. To
address these problems, this paper proposes an explicit EEC control method
named selective-candidate framework with similarity selection rule (SCSS). M (M
> 1) candidates are first generated from each current solution with independent
operations and parameters to enrich the search. Then, a similarity selection
rule is designed to determine the final candidate by considering the fitness
ranking of the current solution and its Euclidian distance to each of these M
candidates. Superior current solutions will prefer the closest candidates for
efficient local exploitation while inferior ones will favor the farthest for
exploration purpose. In this way, the rule could synthesize exploitation and
exploration, making the evolution more effective. When applied to three
classic, four state-of-the-art and four up-to-date EOAs from branches of
differential evolution, evolution strategy and particle swarm optimization,
significant enhancement in performance is achieved