Constraint Consensus Mutation-Based Differential Evolution for Constrained Optimization

Hamza, Noha M.; Essam, Daryl; Sarker, Ruhul A.

doi:10.1109/tevc.2015.2477402

Cited by 55 publications

(22 citation statements)

References 40 publications

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“…In this method, the constraint consensus [43] aims at moving the infeasible solutions along the parallel direction to the violated constraint, thus making them feasible quickly. The constraint consensus has also been used in [44].…”

Section: E ε Constrained Methodsmentioning

confidence: 99%

Composite Differential Evolution for Constrained Evolutionary Optimization

Wang

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

149

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Abstract-When solving constrained optimization problems by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite differential evolution, the feasibility rule and the ε constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated constrained optimization problems. By assembling the above techniques together, a constrained composite differential evolution is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods.

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Section: E ε Constrained Methodsmentioning

confidence: 99%

Composite Differential Evolution for Constrained Evolutionary Optimization

Wang

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

149

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“…DIFFERENTIAL EVOLUTION AND msMS DE ALGORITHM DE was initially proposed in 1990s by Storn and Price [9], [39] and has derived many variants. DE algorithms have witnessed wide applications in diverse domains of science and engineering [10], [38]. In this section, we first briefly introduce the conventional DE algorithm, analyze DE with variant strategies and control parameters, and then propose an improved DE algorithm of msMS DE for these quantum robust control problems presented in Section II.…”

Section: Femtosecond Laser Quantum Controlmentioning

confidence: 99%

Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments

Dong

et al. 2020

IEEE Trans. Cybern.

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Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry and atomic physics. In this paper, an improved differential evolution algorithm of msMS DE is proposed to search robust fields for various quantum control problems. In msMS DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for mutation operation. In particular, the msMS DE algorithm is applied to the control problem of open inhomogeneous quantum ensembles and the consensus problem of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved DE algorithm for these two classes of quantum robust control problems. Furthermore, msMS DE is experimentally implemented on femtosecond laser control systems to generate good signals of twophoton absorbtion and control fragmentation of halomethane molecules CH 2 BrI. Experimental results demonstrate excellent performance of msMS DE in searching effective femtosecond laser pulses for various tasks.

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“…CC strategy, using a variety of projection algorithms to solve the feasibility problems with nonlinear and nonconvex constraints. e key idea is to help a currently infeasible solution to quickly move to the feasible region or move close to the feasible region by constructing a consensus among the currently violated constraints [7,31,32].…”

Section: Constraint Consensusmentioning

confidence: 99%

Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems

Sun

Liang

et al. 2019

Discrete Dynamics in Nature and Society

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Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed.

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Constraint Consensus Mutation-Based Differential Evolution for Constrained Optimization

Cited by 55 publications

References 40 publications

Composite Differential Evolution for Constrained Evolutionary Optimization

Composite Differential Evolution for Constrained Evolutionary Optimization

Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments

Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems

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