Social Network-based Swarm Optimization algorithm

Liang, Xiaolei; Li, Wenfeng; Li, Panpan; Zhang, Yu; Agbo, Aaron Agbenyegah

doi:10.1109/icnsc.2015.7116063

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…On analyzing the behavior of the swarm from the perspective of population, their neighborhood and individual behavior, a new SI algorithm named as social network-based swarm optimization algorithm (SNSO) was proposed by Xiaolei Liang et al (2015). This algorithm was proposed to solve unconstrained single-objective optimization problems.…”

Section: Related Workmentioning

confidence: 99%

A PSO based investigation of research fields of researchers

Ghosh

Akki

Kasturi

2019

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Purpose The purpose of this study is data generated from any social networking sites may provide some hidden knowledge on a particular domain. Based on this concept the previous paper had proved that social connectivity enhancement takes place through triadic closure and embeddedness in terms of social network graph-theoretic approach. Further, the work was justified by genetic algorithm (GA) where observation showed how interdisciplinary work can occur because of crossover, and therefore, different groups of researchers could be identified. Further enhancement of the work has been focused on in this paper. Design/methodology/approach In continuation with the previous work, this paper detects other possible fields related to “high graded researchers” who can share the information with the other group of researchers (“imminent high graded” and “new researchers”) using particle swarm optimization (PSO) technique. Findings While exploitation was done using GA in the previous work, exploration is done in the current work based on PSO using the same grade score value to the objective function. Both the velocity and direction of high graded researchers in this extended work could be derived, which was not possible using GA. Originality/value This could help the next two levels of researchers (“imminent high graded researchers” and “new researchers”) in expanding their research fields in line with the fields of high graded researchers.

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Section: Related Workmentioning

confidence: 99%

A PSO based investigation of research fields of researchers

Ghosh

Akki

Kasturi

2019

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“…In the literature of PSO, it is well recognized that the learning strategy of particles plays a key role in helping PSO achieve promising performance [8,[34][35][36][37]. As a result, researchers have focused significant efforts in designing effective learning strategies for PSO to improve its performance, and thus many remarkable novel learning schemes have emerged, such as cooperative learning mechanisms [38,39], comprehensive learning strategies [8,40], and social learning methods [41,42]. In fact, the key to devising effective learning strategies is to select appropriate guiding exemplars for particles to update.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

et al. 2022

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Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants.

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“…Social Network-based Swarm Optimization algorithm (SNSO) is applied for analyzing the unconstrained single objective optimization problems. The execution of SNSO is associated with seven other swarm algorithms on twelve familiar benchmark functions [37]. Population-based algorithms integrated with data mining techniques and more efficient algorithms are proposed in order to understand better and to solve the realtime Big data analytics.…”

Section: Related Workmentioning

confidence: 99%