Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization

Tae Jong Choi,Yeonju Lee,Nader Asnafi

doi:10.1051/matecconf/201818903020

Tae Jong Choi, Yeonju Lee + Show 1 more

Open Access

https://doi.org/10.1051/matecconf/201818903020

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Abstract

In this paper, we propose an extended self-adaptive differential evolution algorithm, called A-jDE. A-jDE algorithm is based on jDE algorithm with the asynchronous method. jDE algorithm is one of the popular DE variants, which shows robust optimization performance on various problems. However, jDE algorithm uses a slow mutation strategy so that its convergence speed is slow compared to several state-of-the-art DE algorithms. The asynchronous method is one of the recently investigated approaches that if it finds a better solution, the solution is included in the current population immediately so it can be served as a donor individual. Therefore, it can improve the convergence speed significantly. We evaluated the optimization performance of A-jDE algorithm in 13 scalable benchmark problems on 30 and 100 dimensions. Our experiments prove that incorporating jDE algorithm with the asynchronous method can improve the optimization performance in not only a unimodal benchmark problem but also multimodal benchmark problem significantly.

Highlights

Differential Evolution (DE) algorithm proposed by Storn and Price [1] is a popular evolutionary algorithm especially for solving continuous domain optimization problems
If Asynchronous DE (ADE) algorithm finds a better solution, the solution is included in the current population immediately so it can be served as a donor individual
We presented an extended self-adaptive DE algorithm, A-jDE. jDE algorithm shows robust optimization performance on various problems, but it uses the slow mutation strategy, DE/rand/1

Summary

Introduction

Differential Evolution (DE) algorithm proposed by Storn and Price [1] is a popular evolutionary algorithm especially for solving continuous domain optimization problems. Since DE algorithm was proposed, many studies have been conducted for improving its searchability, such as adaptive parameter control [5,6,7,8,9,10], adaptive strategy control [11], and hybridizing DE algorithm with other methods [12], to solve more complicated optimization problems. We extend jDE algorithm [5], which is one of the state-of-the-art DE algorithms, with the asynchronous method and test its optimization performance in 13 scalable benchmark problems [13] on 30 and 100 dimensions. The optimization performance of the proposed algorithm outperformed the compared algorithms significantly This implies that the selfadaptive parameter control method still works well along with asynchronous DE algorithm

Classical DE Algorithm

Asynchronous DE Algorithm

Asynchronous DE with Self-Adaptive Parameter Control

Simulation

Findings

Conclusion