Multi objective Fitness Dependent Optimizer Algorithm

TL;DR

This paper introduces MOFDO, a multi-objective optimization algorithm that extends FDO by incorporating five types of knowledge, demonstrating superior performance on benchmark functions and real-world engineering problems.

Contribution

The paper presents MOFDO, a novel multi-objective optimizer that integrates multiple knowledge types, outperforming existing algorithms on standard benchmarks and real-world applications.

Findings

MOFDO outperforms MOPSO, NSGA-III, and MODA on benchmark functions.

MOFDO provides well-distributed feasible solutions for engineering problems.

The algorithm demonstrates versatility across different optimization scenarios.

Abstract

This paper proposes the multi objective variant of the recently introduced fitness dependent optimizer (FDO). The algorithm is called a Multi objective Fitness Dependent Optimizer (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and historical knowledge) as in FDO. MOFDO is tested on two standard benchmarks for the performance-proof purpose; classical ZDT test functions, which is a widespread test suite that takes its name from its authors Zitzler, Deb, and Thiele, and on IEEE Congress of Evolutionary Computation benchmark (CEC 2019) multi modal multi objective functions. MOFDO results are compared to the latest variant of multi objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm third improvement (NSGA-III), and multi objective dragonfly algorithm (MODA). The comparative study shows the superiority of MOFDO in most cases and comparative results in other cases. Moreover, MOFDO is used for optimizing real-world engineering problems (e.g., welded beam design problems). It is observed that the proposed algorithm successfully provides a wide variety of well-distributed feasible solutions, which enable the decision-makers to have more applicable-comfort choices to consider.