Hybrid evolutionary computation for the development of pollution prevention and control strategies

Particle swarm optimization (PSO) is an evolutionary algorithm based on the behavior of social animals. Its key advantage is its computational efficiency compared to related techniques such as genetic algorithm (GA). Use of a modified PSO algorithm in selecting an optimal array of pollution prevention techniques for clay brick production is described. The model is formulated as a multi-constraint knapsack optimization problem. The optimization technique used in the study is a binary PSO augmented with a GA-based mutation operator.