MANY OBJECTIVE OPTIMIZATION PROBLEM USING BAT ALGORITHM BASED ON INVERTED GENERATIONAL INDICATOR

Abstract

The problems around us are becoming more complex at the same time and at the same time our mother nature is guiding us to solve these natural problems. Nature offers us some logical and effective ways to find a solution to these problems.

Although computational strategies for taking care of Many-objective Optimization Problems (MOPs/I) have been accessible for a long time, the ongoing utilization of Evolutionary Algorithm (EAs) to such issues gives a vehicle which to tackle extremely enormous scope MOPs/I.

MOBAT/I is a many-objective bat algorithm that incorporates the dominance concept with the decomposition approach is proposed. Whilst decomposition simplifies the multi-objective problem (MOP) by rewriting it as a set of Tchebycheff Approach, solving these problems simultaneously, within the BAT framework, might lead to premature convergence because of the leader selection process which uses the Tchebycheff Approach as a criterion. Dominance plays a major role in building the leaders archive allowing the selected leaders to cover less dense regions avoiding local optima and resulting in a more diverse approximated Pareto front. Results from 35 standard MOPs show MOBAT/I it outperforms some developmental methods based on decomposition. All the results were done by MATLAB (R2017b).