Multiobjective thermal power load dispatch using adaptive predator–prey optimization

This paper presents an adaptive predator–prey optimization (APPO) to solve multiobjective thermal power dispatch problem considering objectives of operating cost and pollutant emission. In APPO the fear factor from a predator is a function of cognitive and social behavior of prey. It ensures continuous mobility (magnitude and direction) of prey, resulting in better diversification of solutions. The velocity of prey is maintained in the limits by recognizing the reinforcement and inhibition aspects. The multiobjective optimization problem is handled by weighting method, whereby the weight pattern assigned to the objectives has been undertaken as a decision variable. This results in non-inferior solutions at each swarm move. In order to select a best-compromised solution, the fuzzy theory is used. The performance of the proposed algorithm is investigated on six power system test problems. The proposed method provides better results in terms of lesser fuel cost and pollutant emission. The better satisfaction level of conflicting objectives, well distributed Pareto front, acceptable solution in a single trial run and insensitivity to parameter variations are observed in comparison to other existing methods reported in the literature.