Adaptation of operators and continuous control parameters in differential evolution for constrained optimization

Elsayed, Saber M.; Sarker, Ruhul A.; Coello, Carlos A. Coello; Ray, Tapabrata

doi:10.1007/s00500-017-2712-6

Cited by 24 publications

(8 citation statements)

References 54 publications

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“…In order to further explore the ability of ImKPSO to explore complex problems, in this section, we test ImKPSO on 9 functions in CEC2017 (including 10D and 30D), and select two state-of-the-art algorithms DE-AOPS [32] and GLoSADE [33] to compare with ImKPSO. The following is the brief information of these two comparison algorithms: DE-AOPS: An improved differential evolution algorithm that can adaptively select the scaling factor F and the cross-factor CR within a given continuous range.…”

Section: Comparison With Two State-of-the-art Methods In Cec2017mentioning

confidence: 99%

Data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Fan¹,

Hou²,

Qu³

et al. 2021

Preprint

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The economic operation optimization of microgrid is an important research topic in the power system. Therefore, this paper proposes a surrogate model particle swarm optimization algorithm based on the global-local search mechanism. Firstly, aiming at the problem that the statistical information of Kriging model is difficult to guarantee the prediction accuracy, the dynamic transformation is carried out to enhance the robustness of the model; secondly, the global-local search mechanism is introduced to make the algorithm fully explore the fitness landscape near the Kriging model after quickly locating the current optimal particle position, so as to achieve the balance of convergence quality and convergence efficiency. The proposed method has been tested on numerous benchmark test functions from two test suites, and the results show that the proposed algorithm has more advantages than other comparison algorithms in optimization accuracy. Finally, simulations are carried out in two operating modes of microgrid islanding and grid-connected, which has verified the effectiveness of the proposed method.

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Section: Comparison With Two State-of-the-art Methods In Cec2017mentioning

confidence: 99%

Data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Fan¹,

Hou²,

Qu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…One way of dealing with this issue is to use a subset of the constraints to calculate the total constraints violations, and gradually increase this number until the total number of constraints are handled. In this paper, we used the constrained handling technique proposed in [48]. The method starts by sorting all the constraints based on their sum of the constraint violations of all the solutions in the initial population.…”

Section: Constraints Handlingmentioning

confidence: 99%

“…The value of φ was set to 0.11 for the first two DE operators and 0.5 for the third, the archive rate (A) to 1.4, and the memory size (L) to 5 [29]. The window size, W, was set at a value of 50 [48]. Table 5 presents the summary of the results obtained from the seven variants.…”

Section: Effect Of Number Of Used De Operatorsmentioning

confidence: 99%

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

Hossain¹,

Sallam²,

Elsayed³

et al. 2021

Preprint

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In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow (OPF) problem and is called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution (DE) operator in a single algorithmic framework. Additionally, an adaptive method (AM) is proposed to update the number of solutions evolved by each DE operator based on both the diversity of population and quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering the intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results and thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%.

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“…Instead of being limited to only bound constrained problems, the authors in [5] developed a new mechanism for automatically choosing the best combinations of DE parameters to solve constrained optimization problems. In addition, their proposed approach was also shown to be successful for selecting the search operators and handling multiple constraints.…”

Section: ) Multi-operator De Variantsmentioning

confidence: 99%

“…As with all other EAs, DE uses three main operators (mutation, crossover and selection) to guide a set of solutions to obtain acceptable results. DE has attracted the attention of many researchers and practitioners because of its ease of use, its simple structure, and its robustness [4], [5]. Covariance matrix adaptation evolution strategy (CMA-ES) [6] has also attracted the attention of researchers [4], [7].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

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Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of stateof-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.

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Adaptation of operators and continuous control parameters in differential evolution for constrained optimization

Cited by 24 publications

References 54 publications

Data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

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