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

Author(s): Andre A. Keller

DOI: 10.2174/9781681087054119010008

Swarm Intelligence and Co-Evolutionary Algorithms

Pp: 157-172 (16)

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Abstract

SHS investigation development is considered from the geographical and historical viewpoint. 3 stages are described. Within Stage 1 the work was carried out in the Department of the Institute of Chemical Physics in Chernogolovka where the scientific discovery had been made. At Stage 2 the interest to SHS arose in different cities and towns of the former USSR. Within Stage 3 SHS entered the international scene. Now SHS processes and products are being studied in more than 50 countries.

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.

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