<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Differential evolution algorithm (DE) is a well-known population-based method for solving continuous optimization problems. It has a simple structure and is easy to adapt to a wide range of applications. However, with suitable population sizes, its performance depends on the two main control parameters: scaling factor (<italic>F</italic> ) and crossover rate (<italic>CR</italic>). The classical DE method can achieve high performance by a time-consuming tunning process or a sophisticated adaptive control implementation. We propose in this paper an adaptive differential evolution algorithm with a pheromone-based learning strategy (ADE-PS) inspired by ant colony optimization (ACO). The ADE-PS embeds a pheromone-based mechanism that manages the probabilities associated with the partition values of <italic>F</italic> and <italic>CR</italic>. It also introduces a resetting strategy to reset the pheromone at a specific time to unlearn and relearn the progressing search. The preliminary experiments find a suitable number of subintervals (<italic>ns</italic>) for partitioning the control parameter ranges and the reset period (<italic>rs</italic>) for resetting the pheromone. Then the comparison experiments evaluate ADE-PS using the suitable <italic>ns</italic> and <italic>rs</italic> against some adaptive DE methods in the literature. The results show that ADE-PS is more reliable and outperforms several well-known methods in the literature.</p>
</abstract>

Differential evolution algorithm (DE) is a well-known population-based method for solving continuous optimization problems. It has a simple structure and is easy to adapt to a wide range of applications. However, with suitable population sizes, its performance depends on the two main control parameters: scaling factor (F ) and crossover rate (CR). The classical DE method can achieve high performance by a time-consuming tunning process or a sophisticated adaptive control implementation. We propose in this paper an adaptive differential evolution algorithm with a pheromone-based learning strategy (ADE-PS) inspired by ant colony optimization (ACO). The ADE-PS embeds a pheromone-based mechanism that manages the probabilities associated with the partition values of F and CR. It also introduces a resetting strategy to reset the pheromone at a specific time to unlearn and relearn the progressing search. The preliminary experiments find a suitable number of subintervals (ns) for partitioning the control parameter ranges and the reset period (rs) for resetting the pheromone. Then the comparison experiments evaluate ADE-PS using the suitable ns and rs against some adaptive DE methods in the literature. The results show that ADE-PS is more reliable and outperforms several well-known methods in the literature.

Adaptive differential evolution algorithm with a pheromone-based learning strategy for global continuous optimization

Deparment of Mathematics, Faculty of Science

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Differential evolution algorithm (DE) is a well-known population-based method for solving continuous optimization problems. It has a simple structure and is...

Adaptive differential evolution algorithm with a pheromone-based learning strategy for global continuous optimization

Published Online: Jun 30, 2023

Page range: 243 - 266

Received: Mar 08, 2022

Accepted: Dec 15, 2022

DOI: https://doi.org/10.2478/fcds-2023-0010

Keywords
Differential evolution algorithm, ant colony optimization, pheromone strategy, optimization

© 2023 Pirapong Singsathid et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Adaptive differential evolution algorithm with a pheromone-based learning strategy for global continuous optimization

Published Online: Jun 30, 2023

Page range: 243 - 266

Received: Mar 08, 2022

Accepted: Dec 15, 2022

DOI: https://doi.org/10.2478/fcds-2023-0010

KeywordsDifferential evolution algorithm, ant colony optimization, pheromone strategy, optimization

© 2023 Pirapong Singsathid et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Differential evolution algorithm, ant colony optimization, pheromone strategy, optimization