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Conflict Resolution Problem Solving with Bio-Inspired Metaheuristics: A Perspective
Abstract
This chapter addresses nature and bio-inspired metaheuristics in the context of conflict detection and resolution problems. An approach is presented for a generalization of a population-based bio-inspired search and optimization algorithm, which is depicted for three of the most well-known and firmly established methods: the genetic algorithm, the particle swarm optimization algorithm and the differential evolution algorithm. This integrated approach to a basic general population-based bio-inspired algorithm is presented for single-objective optimization, multi-objective optimization and many-objective optimization. A revision of these three main bio-inspired algorithms is presented for conflict resolution problems in diverse application areas. A bridge between feedback controller design, genetic algorithm, particle swarm optimization and differential evolution is established using a conflict resolution approach. Finally, some perspectives concerning future trends of more recent bio-inspired meta-heuristics is presented.
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