A review of techniques for online control of parameters in swarm intelligence and evolutionary computation algorithms

Parpinelli, Rafael Stubs; Plichoski, Guilherme Felippe; Silva, Renan Samuel da; Narloch, Pedro Henrique

doi:10.1504/ijbic.2019.097731

Cited by 51 publications

(21 citation statements)

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“…where P 1 and P 2 are two selected individuals to do crossover operator. beta indicates a vector [3]. Each dimension of beta corresponds to a value generated by the crossover probability, and it indicates that whether or not corresponding dimension of solution is updated in the crossover operator.…”

Section: Details Of Mnsga-iimentioning

confidence: 99%

“…In the real world, there exist many engineering optimization problems [1][2][3][4], such as water resource scheduling [5,6], malicious code detection [7], identity authentication [8][9][10], privacy techniques [11,12], green coal production [13][14][15], wireless sensor network Problem [16], software defect prediction method, [17] and so on. Generally, these engineering optimization problems are referred to as multiobjective optimization problems (MOPs), which are featured with two or three conflicting objectives.…”

Section: Introductionmentioning

confidence: 99%

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Nondominated sorting genetic algorithm II with merged strategies for industrial network topology optimization

Wang

2020

Concurrency and Computation

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Summary Fast nondominated sorting genetic algorithm II (NSGA‐II) is a popular multiobjective optimization method. However, the tournament selection strategy for crossover operator suffers from the drawback of repetitively selecting the same individuals, resulting in unsatisfying performance. To alleviate this problem, this article first proposes to employ k‐means clustering strategy to divide the candidate individuals into multiple clusters. After that, the crossover operator are redefined with three crossover individuals, where the first and second individuals are forced to selected from the same cluster and the second and third ones are from different clusters. The newly proposed crossover operator is not only able to alleviate the phenomenon above, but also able to retain the advantage of the original tournament selection strategy. The proposed method is verified on two popular test suits, including DTLZ and ZDT test suit and an industrial network topology optimization problem. Experimental results demonstrate that the proposed method exhibits excellent performance on both the two test suits and the practical network topology optimization.

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Section: Details Of Mnsga-iimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nondominated sorting genetic algorithm II with merged strategies for industrial network topology optimization

Wang

2020

Concurrency and Computation

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“…Multiple algorithms have been proposed in the literature to self adapt the parameters in DE [5]. Some of the most well known ones are presented in this section.…”

Section: Self-adaptive Differential Evolutionmentioning

confidence: 99%

L-SHADE with Alternative Population Size Reduction for Unconstrained Continuous Optimization

Renkavieski¹,

Parpinelli²

2020

Anais Do XI Computer on the Beach - COTB '20

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Differential Evolution (DE) is a powerful and versatile algorithmfor numerical optimization, but one of its downsides is its numberof parameters that need to be tuned. Multiple techniques have beenproposed to self-adapt DE’s parameters, with L-SHADE being oneof the most well established in the literature. This work presentsthe A-SHADE algorithm, which modifies the population size reductionschema of L-SHADE, and also EB-A-SHADE, which applies amutation strategy hybridization framework to A-SHADE. Thesealgorithms are applied to the CEC2013 benchmark set with 100dimensions, and it’s shown that A-SHADE and EB-A-SHADE canachieve competitive results.

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“…In this paper, to tackle a 5G network optimization problem [31,32], Pareto fronts-driven Multi-Objective Cuckoo Search(PMOCS) is proposed. Then three test suits are used to verify the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Pareto fronts-driven Multi-Objective Cuckoo Search for 5G Network Optimization

2020

KSII TIIS

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5G network optimization problem is a challenging optimization problem in the practical engineering applications. In this paper, to tackle this issue, Pareto fronts-driven Multi-Objective Cuckoo Search (PMOCS) is proposed based on Cuckoo Search. Firstly, the original global search manner is upgraded to a new form, which is aimed to strengthening the convergence. Then, the original local search manner is modified to highlight the diversity. To test the overall performance of PMOCS, PMOCS is test on three test suits against several classical comparison methods. Experimental results demonstrate that PMOCS exhibits outstanding performance. Further experiments on the 5G network optimization problem indicates that PMOCS is promising compared with other methods.

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A review of techniques for online control of parameters in swarm intelligence and evolutionary computation algorithms

Cited by 51 publications

References 0 publications

Nondominated sorting genetic algorithm II with merged strategies for industrial network topology optimization

Nondominated sorting genetic algorithm II with merged strategies for industrial network topology optimization

L-SHADE with Alternative Population Size Reduction for Unconstrained Continuous Optimization

Pareto fronts-driven Multi-Objective Cuckoo Search for 5G Network Optimization

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