Non-Dominated Sorting Bidirectional Differential Coevolution

TL;DR

This paper introduces a novel bidirectional differential coevolution algorithm for constrained multiobjective optimization, utilizing differential evolution operators and non-dominated sorting to improve solution quality on benchmark and real-world problems.

Contribution

It presents a new variant of BiCo with DE operators and non-dominated sorting, enhancing optimization performance for CMOPs.

Findings

Outperforms original BiCo on benchmark test suites.

Achieves better solutions on eight real-world CMOPs.

Demonstrates effectiveness of DE operators in coevolutionary algorithms.

Abstract

Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme. Experimental results on two benchmark test suites and eight real-world CMOPs suggested that the proposed model reached better overall performance than the original model.