Dynamic multi-objective evolutionary algorithms for single-objective optimization

Jiao, Ruwang; Zeng, Sanyou; Alkasassbeh, Jawdat S.; Li, Changhe

doi:10.1016/j.asoc.2017.08.030

Cited by 24 publications

(7 citation statements)

References 33 publications

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“…Due the fact that many real-world problems can be formulated as an optimization problem, the popularity of optimization have increased day by day (Ivana Strumberger et al, 2018). Different types of optimization such as combinatorial (Hakli and Uguz, 2017) and continuous (Farnad et al, 2018;Kiran, 2015), single (Asafuddoula et al, 2014) and multi-objective (Jiao et al, 2017;Luo et al, 2018), unconstrained (Sharma et al, 2017) and constrained are applied in accordance with characteristic of problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on Elephant Herding Optimization for Constrained Optimization Problems

Haklı¹

2019

Scitech

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Many real-world problems can be formulated as an optimization problem and they have some constraints generally. To overcome these constraints, bio-inspired algorithms are adapted to constrained optimization using constraint handling methods and some modifications. In this study, a new approach is developed to solve constrained optimization problems with elephant herding optimization algorithm which is a newly-emerging optimization technique. Besides the basic EHO, two EHO variants (EHO-NoB and GL-EHO) are adapted to constrained optimization with this approach. The well-known thirteen constrained benchmark functions are used to analysis the performances of algorithms. Experimental results show that the GL-EHO has a better performance than the basic EHO and other algorithms. In addition, the results of GL-EHO are comparable level with the result of another EHO variant in the literature.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on Elephant Herding Optimization for Constrained Optimization Problems

Haklı¹

2019

Scitech

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“…Evolutionary algorithms (EAs) are promising in solving them by increasing the population diversity. There are many techniques for the EAs to maintain the diversity, such as fitness sharing (Goldberg and Richardson, 1987), crowding (Mengshoel and Goldberg, 1999;Mahfoud, 1993), restricted tournament selection (Harik, 1995), speciation (Li et al, 2002), clearing (Pétrowski, 1996) and dynamic multi-objective techniques (Jiao et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…One issue of this technique is that the MOP is not equivalent to the original SOP. Our group also employed MOEAs to solve SOP (Jiao et al, 2017). In Jiao et al (2017), a SOP is equivalently converted into a dynamic multi-objective optimisation problem (DMOP).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic HypE for solving single objective optimisation problems

Zhang

Zhao²,

Zeng

2019

IJICA

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One difficulty in solving optimisation problems is the handling many local optima. The usual approaches to handle the difficulty are to introduce the niche-count into evolutionary algorithms (EAs) to increase population diversity. In this paper, we introduce the niche-count into the problems, not into the EAs. We construct a dynamic multi-objective optimisation problem (DMOP) for the single optimisation problem (SOP) and ensure both the DMOP and the SOP are equivalent to each other. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the local optima difficulty during the search process. A dynamic version of a multi-objective evolutionary algorithm (DMOEA), specifically, HypE-DE, is used to solve the DMOP; consequently the SOP is solved. Experimental results show that the performance of the proposed method is significantly better than the state-of-the-art competitors on a set of test problems.

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“…The main benefit of this method is reserving the DE updating strategy to binary space, which can solve discrete valued antenna design problems [10]. Antenna array optimisation is usually a multi-modal problem, to improve the exploration of the algorithm, in [11], a technique of gradually reducing the niche radius combined with DE is proposed to solve an antenna array problem. In [12,13], the balanced dynamic DE algorithms have been adopted to suppress the crosspolarisation in conformal antenna arrays.…”

Section: Introductionmentioning

confidence: 99%

Antenna design using dynamic multi‐objective evolutionary algorithm

Jiao

Sun²,

Sun³

et al. 2018

IET Microwaves, Antennas & Propagation

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To enhance the robustness and stability of the antenna design, a novel methodology is presented for modelling the antenna design problem as the constrained optimisation problem (COP). Then a framework is introduced for solving the COP. The framework converts a COP into an equivalent dynamic constrained multi‐objective optimisation problem (DCMOP). A dynamic constrained multi‐objective evolutionary algorithm is applied to address the transformed DCMOP, which means the COP as well as the antenna design problem can be solved. Then it is applied to design three classes of antennas: a wire antenna, a patch antenna and an antenna array. Obtained results of these three kinds of antenna suggest that the proposed methods can well satisfy the antenna design requirements.

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Dynamic multi-objective evolutionary algorithms for single-objective optimization

Cited by 24 publications

References 33 publications

A Novel Approach Based on Elephant Herding Optimization for Constrained Optimization Problems

A Novel Approach Based on Elephant Herding Optimization for Constrained Optimization Problems

Dynamic HypE for solving single objective optimisation problems

Antenna design using dynamic multi‐objective evolutionary algorithm

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