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The cooperative co-evolution (CC) framework is one of the most efficient methods to solve large scale optimization problems. The traditional CC framework divides decision variables into several mutually-exclusive groups. In this paper, we propose the overlapped cooperative co-evolution (OCC) framework for large scale optimization problems. In OCC framework, the decision variables that have strong...
The aim of multimodal optimization is to locate multiple optima of a given problem. Evolutionary algorithms (EAs) are one of the most promising candidates for multimodal optimization. However, due to the use of greedy selection operators, the population of an EA will generally converge to one region of attraction. By incorporating a well-designed selection operator that can facilitate the formation...
Real coded genetic algorithms (RCGAs) have been widely studied and applied to deal with continuous optimization problems for years. However, how to improve the degree of accuracy so as to produce high quality solutions is still one of the main difficulties that RCGAs face with. This paper proposes a novel mutation scheme for RCGAs. The mutation operator is defined as a linear map in the space of chromosomes...
Ant colony optimization (ACO) has been widely and successfully applied to NP-hard combinatorial optimization problems for its strong searching ability and robustness. Recently, several extended ACO algorithms have also been proposed to deal with continuous optimization problems. However, the ACO algorithms always have slow convergence speed and encounter premature convergence in engineering applications...
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