  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.
