Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms

Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms

Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems. This second part focuses on the use of metaheuristic ...
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Swarm Intelligence and Co-Evolutionary Algorithms

Pp. 157-172 (16)

Andre A. Keller

Abstract

Collective strategies are possible within a population. Such situations occur in nature with birds or fishes in flocks. Such swarm intelligence problems are suitable for optimization problem-solving. Other co-evolutionary models implicate two different populations or species which compete. In the predator-prey model, the predator eliminates the weak prey. We show that such a situation can be transposed to optimization problems, for which the predator is one of the objectives, and the preys are feasible solutions. Solving an MOO problem may use different ways. One way consists of decomposing the initial problem into a sequence of subproblems.

Affiliation:

Center for Research in Computer Science Signal and Automatic Control of Lille University of Lille – CNRS France.