Population topologies for particle swarm optimization and differential evolution

Lynn, Nandar; Ali, Mostafa Z.; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.swevo.2017.11.002

Cited by 146 publications

(25 citation statements)

References 68 publications

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“…The predation behavior of a large number of individual individuals reflects the swarm intelligence behavior. The algorithm has broad application prospects in the fields of polynomial function optimization, traveling salesman problem solving and shop scheduling [89].…”

Section: A Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people's requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Section: A Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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“…Evolution-based optimization can optimize the mathematical functions of the technique with continuously changeable parameters and extend to solve discrete optimization problems. This strategy can deliver a high quality of solutions and allows the technique to move toward better solutions in the search space with a population [105,106]. GA is one of the techniques using evolution strategy, which is commonly used for clustering based on selection, crossover, and mutation.…”

Section: Strategy 2: Evolution-based Optimizationmentioning

confidence: 99%

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

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“…ere are a number of research studies to present the improved velocity update strategies for MOPs [34]. For example, in SMPSO [14], the velocity of the particles is constrained in order to avoid the cases in which the velocity becomes too high.…”

Section: Introductionmentioning

confidence: 99%

A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

Chen

Liu

et al. 2020

Complexity

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Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-based method to select the local or best particles, which is not so effective in high-dimensional objective space. To better solve MaOPs, this paper presents a novel angular-guided particle swarm optimizer (called AGPSO). A novel velocity update strategy is designed in AGPSO, which aims to enhance the search intensity around the particles selected based on their angular distances. Using an external archive, the local best particles are selected from the surrounding particles with the best convergence, while the global best particles are chosen from the top 20% particles with the better convergence among the entire particle swarm. Moreover, an angular-guided archive update strategy is proposed in AGPSO, which maintains a consistent population with balanceable convergence and diversity. To evaluate the performance of AGPSO, the WFG and MaF test suites with 5 to 10 objectives are adopted. The experimental results indicate that AGPSO shows the superior performance over four current MOPSOs (SMPSO, dMOPSO, NMPSO, and MaPSO) and four competitive evolutionary algorithms (VaEA, θ-DEA, MOEA\D-DD, and SPEA2-SDE), when solving most of the test problems used.

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Population topologies for particle swarm optimization and differential evolution

Cited by 146 publications

References 68 publications

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

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