A New Solution for Multi-objective Optimization Problem Using Extended Swarm-Based MVMO

Multi-Objective optimization problems corresponds to those problems which are having more than one objective to be optimized together. For such problem rather than an optimal solution, a set of solutions exists which is trade-off among different objectives. There are several solution techniques exist including evolutionary algorithms. Evolutionary algorithm provides Pareto optimal solutions after evolving continuously through many generations of solutions. Mean-variance mapping optimization is a stochastic optimization technique which the swarm hybrid variant works well on single objective optimization problem. The paper aims at extending the swarm hybrid variant of mean variance mapping optimization to a multi-objective optimization technique by incorporating non-dominated sorting and an adaptive local learch strategy. The proposed solution is evaluated on standard benchmark such as DTLZ and ZDT. The evaluation results establish that the proposed solution generates Pareto fronts those are comparable to the true Pareto fronts.