TY - JOUR
T1 - Balanced multi-objective optimization algorithm using improvement based reference points approach
AU - Abdel-Basset, Mohamed
AU - Mohamed, Reda
AU - Abouhawwash, Mohamed
N1 - Publisher Copyright:
© 2020
PY - 2021/2
Y1 - 2021/2
N2 - In this work, we explore a novel multi-objective optimization algorithm to identify a set of solutions that could be optimal for more than one task. The proposed approach is used to generate a set of solutions that balance the tradeoff between convergence and diversity in multi-objective optimization problems. Equilibrium Optimizer (EO) algorithm is a novel developed meta-heuristic algorithm inspired by the physics laws. In this paper, we propose a Multi-objective Equilibrium Optimizer Algorithm (MEOA) for tackling multi-objective optimization problems. We suggest an enhancement for exploration and exploitation factors of the EO algorithm to randomize the values of these factors with decreasing the initial value of the exploration factor with the iteration and increasing the exploitation factor to accelerate the convergence toward the best solution. To achieve good convergence and well-distributed solutions, the proposed algorithm is integrated with the Improvement-Based Reference Points Method (IBRPM). The proposed approach is applied to the CEC 2020, CEC 2009, DTLZ, and ZDT test functions. Also, the inverted generational and spread spacing metrics are used to compare the proposed algorithm with the most recent evolutionary algorithms. It's obvious from the results that the proposed algorithm is better in both convergence and diversity.
AB - In this work, we explore a novel multi-objective optimization algorithm to identify a set of solutions that could be optimal for more than one task. The proposed approach is used to generate a set of solutions that balance the tradeoff between convergence and diversity in multi-objective optimization problems. Equilibrium Optimizer (EO) algorithm is a novel developed meta-heuristic algorithm inspired by the physics laws. In this paper, we propose a Multi-objective Equilibrium Optimizer Algorithm (MEOA) for tackling multi-objective optimization problems. We suggest an enhancement for exploration and exploitation factors of the EO algorithm to randomize the values of these factors with decreasing the initial value of the exploration factor with the iteration and increasing the exploitation factor to accelerate the convergence toward the best solution. To achieve good convergence and well-distributed solutions, the proposed algorithm is integrated with the Improvement-Based Reference Points Method (IBRPM). The proposed approach is applied to the CEC 2020, CEC 2009, DTLZ, and ZDT test functions. Also, the inverted generational and spread spacing metrics are used to compare the proposed algorithm with the most recent evolutionary algorithms. It's obvious from the results that the proposed algorithm is better in both convergence and diversity.
KW - CEC 2009
KW - CEC 2020
KW - Equilibrium optimizer
KW - Multi-objective optimization problem
KW - Reference directions
UR - http://www.scopus.com/inward/record.url?scp=85094320312&partnerID=8YFLogxK
U2 - 10.1016/j.swevo.2020.100791
DO - 10.1016/j.swevo.2020.100791
M3 - Article
AN - SCOPUS:85094320312
SN - 2210-6502
VL - 60
SP - 1
EP - 23
JO - Swarm and Evolutionary Computation
JF - Swarm and Evolutionary Computation
M1 - 100791
ER -