AMGA

Abstract

In this paper, we propose a new evolutionary algorithm for multi-objective optimization. The proposed algorithm benefits from the existing literature and borrows several concepts from existing multi-objective optimization algorithms. The proposed algorithm employs a new kind of selection procedure which benefits from the search history of the algorithm and attempts to minimize the number of function evaluations required to achieve the desired convergence. The proposed algorithm works with a very small population size and maintains an archive of best and diverse solutions obtained so as to report a large number of non-dominated solutions at the end of the simulation. Improved formulation for some of the existing diversity preservation techniques is also proposed. Certain implementation aspects that facilitate better performance of the algorithm are discussed. Comprehensive benchmarking and comparison of the proposed algorithm with some of the state-of-the-art multi-objective evolutionary algorithms demonstrate the improved search capability of the proposed algorithm.