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MCACEA (Multiple Coordinated Agents Coevolution Evolutionary Algorithm) is a general framework that uses a single evolutionary algorithm (EA) per agent sharing their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives. This framework can be used to optimize some characteristics of multiple cooperating agents in mathematical optimization problems. More specifically, due to its nature in which both individual and cooperation objectives are optimize, MCACEA is used in multi-objective optimization problems. ==Description and implementation== MCACEA, uses multiple EAs (one per each agent) that evolve their own populations to find the best solution for its associated problem according to their individual and cooperation constraints and objective indexes. Each EA is an optimization problem that runs in parallel and that exchanges some information with the others during its evaluation step. This information is needed to let each EA to measure the coordination objectives of the solutions encoded in its own population, taking into account the possible optimal solutions of the remaining populations of the other EAs. With this purpose, each single EA receives information related to the best solutions of the remaining ones before evaluating the cooperative objectives of each possible solution of its own population. As the cooperation objective values depend on the best solutions of the other populations and the optimality of a solution depends both on the individual and cooperation objectives, it is not really possible to select and send the best solution of each planner to the others. However, MCACEA divides the evaluation step inside each EA in three parts: In the first part, the EAs identify the best solution considering only its individual objective values and send it to the others EAs; in the second part, the cooperation objective values of all solutions are calculated taking into account the received information; and in the third part, the EAs calculate the fitness of the solutions considering all the individual and cooperation objective values. Although each population can only offer a unique optimal solution, each EA maintains a pareto set of optimal solutions and selects the unique optimal solution at the end, when the last population has already been obtained. Therefore, to be able to determine a unique optimal solution according with the individual objectives in each generation (and so, using it with the MCACEA framework), a step in charge of selecting the final optimal solution must also be included in the evaluation step of each EA. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「MCACEA」の詳細全文を読む スポンサード リンク
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