Improved differential evolution for dynamic optimization problems

Plessis, Mathys C. du; Engelbrecht, Andries P.

doi:10.1109/cec.2008.4630804

Cited by 20 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More details regarding DynDE is given in later sections. The favoured populations DE (FPDE) [18] is an adaptation to DynDE. The adaptation aims to prevent wasting function evaluations on sub-populations with inferior performance.…”

Section: Differential Evolution-based Algorithmsmentioning

confidence: 99%

Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments

Plessis

Engelbrecht

Calitz

2015

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII

Self Cite

View full text Add to dashboard Cite

Several algorithms aimed at dynamic optimisation problems have been developed. This paper reports on the incorporation of a self-adaptive Brownian radius into competitive differential evolution (CDE). Four variations of a novel technique to achieving the self-adaptation is suggested and motivated. An experimental investigation over a large number of benchmark instances is used to determine the most effective of the four variations. The new algorithm is compared to its base algorithm on an extensive set of benchmark problems and its performance analysed. Finally, the new algorithm is compared to other algorithms by means of reported results found in the literature. The results indicate that CDE is improved the the incorporation of the self-adaptive Brownian radius and that the new algorithm compares well with other algorithms.

show abstract

Section: Differential Evolution-based Algorithmsmentioning

confidence: 99%

Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments

Plessis

Engelbrecht

Calitz

2015

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [63], if the Euclidean distance between two subpopulations' best found positions (e.g., Gbest in PSO) becomes less than a predefined radius r excl , then the subpopulation with better best found position is kept and the other one will be randomized. This method that acts based on the Euclidean distance between subpopulations' best found positions, is known as exclusion method and is used in many DOAs [70], [86]- [88]. Parameter r excl is defined differently in the literature.…”

Section: Diversity Controlmentioning

confidence: 99%

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part A

Yazdani

Cheng

Yazdani

et al. 2021

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

show abstract

“…It has been shown that Differential evolution algorithm (DE) [11] is be a simple and powerful algorithm for continuous function optimization, not even in static [12] but also in dynamic environments [7,[13][14][15]. Moreover, DynDE [7], which to the best of our knowledge is the best-performing differential evolution algorithm for dynamic optimization problems, produces competitive results compared to other dynamic optimization algorithms.…”

Section: Introductionmentioning

confidence: 97%

“…Moreover, DynDE [7], which to the best of our knowledge is the best-performing differential evolution algorithm for dynamic optimization problems, produces competitive results compared to other dynamic optimization algorithms. Although an improved version of DynDE [13] is presented, it only improves the performance of a few environments a little, while it significantly increases the complexity of the algorithm.…”

Section: Introductionmentioning

confidence: 97%

CellularDE: A Cellular Based Differential Evolution for Dynamic Optimization Problems

Noroozi

Hashemi

Meybodi

2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In real life we are often confronted with dynamic optimization problems whose optima change over time. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In this paper, we propose an evolutionary model that combines the differential evolution algorithm with cellular automata to address dynamic optimization problems. In the proposed model, called CellularDE, a cellular automaton partitions the search space into cells. Individuals in each cell, which implicitly create a subpopulation, are evolved by the differential evolution algorithm to find the local optimum in the cell neighborhood. Experimental results on the moving peaks benchmark show that CellularDE outperforms DynDE, cellular PSO, FMSO, and mQSO in most tested dynamic environments.

show abstract

Improved differential evolution for dynamic optimization problems

Cited by 20 publications

References 5 publications

Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments

Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part A

CellularDE: A Cellular Based Differential Evolution for Dynamic Optimization Problems

Contact Info

Product

Resources

About