Multi-objective optimization using big-bang big-crunch based optimization : application to optimal economic environmental dispatch

Abstract EN

This paper attempts to apply Big-Bang and Big-crunch (BB-BC) based evolutionary algorithm to solve Multi-objective (M0) Optimization problem by vector optimization approach.

A reformulated version referred to as modified BB-BC (MBB-BC) is used to overcome the deficiency of BB-BC.

The crowding distance measure is included in the proposed M0 algorithm to achieve the spread among obtained non-dominated solutions.

The Economic Environmental Dispatch (EED) objectives are minimized by vector approach while maintaining equality and inequality constraints.

The proposed algorithm is tested for bench mark MO test functions of varying difficulty in converging to a true Pareto front.

As an application to optimal EED, an eleven buses and IEEE-30 bus transmission utilities are considered.

The convergence and spread metrics are used to assess the extent of success.

The best compromise solutions are reported using Fuzzy decision making approach.