Advancements in Optimization: Adaptive Differential Evolution with Diversification Strategy

TL;DR

This paper introduces ADEDS, an adaptive differential evolution algorithm with diversification strategies, which demonstrates superior performance over standard DE on various benchmark functions, promising improvements in complex optimization tasks.

Contribution

The paper proposes ADEDS, a novel adaptive differential evolution algorithm with diversification strategies, validated on 22 benchmark functions, showing improved convergence and solution quality.

Findings

ADEDS outperforms standard DE on multiple benchmark functions.

ADEDS effectively handles functions with many local optima and noise.

The algorithm shows potential for supply chain optimization improvements.

Abstract

This study presents a population-based evolutionary optimization algorithm (Adaptive Differential Evolution with Diversification Strategies or ADEDS). The algorithm developed using the sinusoidal objective function and subsequently evaluated with a wide-ranging set of 22 benchmark functions, including Rosenbrock, Rastrigin, Ackley, and DeVilliersGlasser02, among others. The study employs single-objective optimization in a two-dimensional space and runs ADEDS on each of the benchmark functions with multiple iterations. In terms of convergence speed and solution quality, ADEDS consistently outperforms standard DE for a variety of optimization challenges, including functions with numerous local optima, plate-shaped, valley-shaped, stretched-shaped, and noisy functions. This effectiveness holds great promise for optimizing supply chain operations, driving cost reductions, and ultimately enhancing overall performance. The findings imply the importance of effective optimization strategy for improving supply chain efficiency, reducing costs, and enhancing overall performance.