Orthogonal learning particle swarm optimization

Zhan, Zhi-Hui; Zhang, Mengjie; Liu, Ou

doi:10.1145/1569901.1570147

Cited by 86 publications

(126 citation statements)

References 6 publications

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“…To indicate the performance of our proposed CSM-based multi-operator search strategy, it is implemented as the multi-operator search for three advanced EAs: JADE [8], OLPSO [9], and CoBiDE [10]. As an illustration, it is also used to implement the hybrid DE and PSO, in which different DE operators and PSO operators are used.…”

Section: E Remarksmentioning

confidence: 99%

“…In this section, CSM is integrated into an advanced PSO variant, that is, OLPSO [9]. OLPSO is an orthogonal learning based PSO, where two variants are proposed, that is, OLPSO-G and OLPSO-L.…”

Section: ) Csm-based Olpsomentioning

confidence: 99%

“…To this end, the CSM is first integrated into three advanced EAs: JADE [8], OLPSO [9], and CoBiDE [10], to implement a multi-operator ensemble for each of them. Secondly, we try to implement a hybrid DE and PSO method by combining DE and PSO search operators based on the CSM.…”

mentioning

confidence: 99%

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A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization

Gong

Zhou

Cai

2015

IEEE Trans. Evol. Computat.

123

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It is well known that in evolutionary algorithms, different reproduction operators may be suitable for different problems or in different running stages. To improve the algorithm performance, the ensemble of multiple operators has become popular. Most ensemble techniques achieve this goal by choosing an operator according to a probability learned from the previous experience. In contrast to these ensemble techniques, in this paper we propose a cheap surrogate model based multi-operator search strategy for evolutionary optimization. In our approach, a set of candidate offspring solutions are generated by using the multiple offspring reproduction operators, and the best one according to the surrogate model is chosen as the offspring solution. Two major advantages of this approach are (a) each operator can generate a solution for competition compared to the probability based approaches, and (b) the surrogate model building is relatively cheap compared to that in the surrogateassisted evolutionary algorithms. The model is used to implement multi-operator ensemble in two popular evolutionary algorithms, that is, differential evolution and particle swarm optimization. Thirty benchmark functions and the functions presented in the CEC 2013 are chosen as the test suite to evaluate our approach. Experimental results indicate that the new approach can improve the performance of single operator based methods in the majority of the functions.Index Terms-Evolutionary algorithm, global optimization, multi-operator ensemble, surrogate model.

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Section: E Remarksmentioning

confidence: 99%

Section: ) Csm-based Olpsomentioning

confidence: 99%

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A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization

Gong

Zhou

Cai

2015

IEEE Trans. Evol. Computat.

123

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“…Interested readers can refer to [27] and [28] for enhanced PSOs and [29] for PSO industrial applications.…”

Section: B Pso In the Continuous Domainmentioning

confidence: 99%

Bi-Velocity Discrete Particle Swarm Optimization and Its Application to Multicast Routing Problem in Communication Networks

Shen

Zhan

Chen

et al. 2014

IEEE Trans. Ind. Electron.

Self Cite

112

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This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the nondeterministic polynomial (NP) complete multicast routing problem (MRP). The main contribution is the extension of particle swarm optimization (PSO) from the continuous domain to the binary or discrete domain. First, a novel bi-velocity strategy is developed to represent the possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP, where 1 stands for a node being selected to construct the multicast tree, whereas 0 stands for being otherwise. Second, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in the continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the Operation Research Library (OR-library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly since it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on genetic algorithms, ant colony optimization, and PSO.Index Terms-Communication networks, multicast routing problem (MRP), particle swarm optimization (PSO), Steiner tree problem (STP).

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“…Take PSO as an example. In the last decades, many enhanced PSO versions were developed, such as comprehensive learning PSO [2], mimetic fitness Euclidean-distance PSO [3], orthogonal learning PSO [4], and PSO with local search [5]. According to no free lunch theory [6], no algorithm will be effective for all optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Complexity Reduction in the Use of Evolutionary Algorithms to Function Optimization: A Variable Reduction Strategy

Pedrycz

et al. 2013

The Scientific World Journal

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Discovering and utilizing problem domain knowledge is a promising direction towards improving the efficiency of evolutionary algorithms (EAs) when solving optimization problems. We propose a knowledge-based variable reduction strategy (VRS) that can be integrated into EAs to solve unconstrained and first-order derivative optimization functions more efficiently. VRS originates from the knowledge that, in an unconstrained and first-order derivative optimization function, the optimal solution locates in a local extreme point at which the partial derivative over each variable equals zero. Through this collective of partial derivative equations, some quantitative relations among different variables can be obtained. These variable relations have to be satisfied in the optimal solution. With the use of such relations, VRS could reduce the number of variables and shrink the solution space when using EAs to deal with the optimization function, thus improving the optimizing speed and quality. When we apply VRS to optimization problems, we just need to modify the calculation approach of the objective function. Therefore, practically, it can be integrated with any EA. In this study, VRS is combined with particle swarm optimization variants and tested on several benchmark optimization functions and a real-world optimization problem. Computational results and comparative study demonstrate the effectiveness of VRS.

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Orthogonal learning particle swarm optimization

Cited by 86 publications

References 6 publications

A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization

A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization

Bi-Velocity Discrete Particle Swarm Optimization and Its Application to Multicast Routing Problem in Communication Networks

Complexity Reduction in the Use of Evolutionary Algorithms to Function Optimization: A Variable Reduction Strategy

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