Hybrid NSGAII-MOPSO Algorithm

hybrid metaheuristic algorithm a merger of Nondominated Sorting Genetic Algorithm II and Multiobjective Particle Swarm Optimization

This research work proposes a synergistic hybrid metaheuristic algorithm a merger of Nondominated Sorting Genetic Algorithm II and Multiobjective Particle Swarm Optimization algorithm for solving multi objective test function. During the iteration, based on ranking, the population is divided into two halves. The exploration is carried out by Nondominated Sorting Genetic Algorithm II using the upper half of the population. The modification of Multiobjective Particle Swarm Optimization to effectively exploit the lower half of the population is done by increasing the personal learning coefficient, decreasing the global learning coefficient and by using an adaptive mutation operator. The proposed hybrid algorithm with an effective constraint handling mechanism enhances the searching capability by effective information interchange. The algorithm is applied to standard test functions. The hybrid algorithm can obtain a well spread and diverse Pareto optimal solution and also can converge to the actual Pareto optimal front faster than some of the existing algorithms.

The test functions are avialable in the following works:

A. Sundaram, "Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism," in IEEE Access, vol. 8, pp. 13748-13768, 2020, doi: 10.1109/ACCESS.2020.2963887.

Arunachalam Sundaram (December 20th 2017). Solution of Combined Economic Emission Dispatch Problem with Valve-Point Effect Using Hybrid NSGA II-MOPSO, Particle Swarm Optimization with Applications, Pakize Erdoğmuş, IntechOpen, DOI: 10.5772/intechopen.72807. Available from: https://www.intechopen.com/books/particle-swarm-optimization-with-applications/solution-of-combined-economic-emission-dispatch-problem-with-valve-point-effect-using-hybrid-nsga-ii