Enhancing Particle Swarm Optimization with Socio-cognitive Inspirations

Bugajski, Iwan I.; Listkiewicz, Piotr; Byrski, Aleksander; Kisiel-Dorohinicki, Marek; Korczyński, Wojciech; Lenaerts, Tom; Samson, Dana; Indurkhya, Bipin; Now, Ann

doi:10.1016/j.procs.2016.05.370

Cited by 11 publications

(7 citation statements)

References 8 publications

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“…b) The Socio-Cognitively Inspired PSO [22]: This approach divides the PSO swarm into sub-swarms, called species because they have unique evolution mechanisms. The particles get inspired by the global and the local optima, but they also share their knowledge of optimal locations with neighboring particles belonging to other species.…”

Section: B Multi-swarm Pso Approachesmentioning

confidence: 99%

“…The multi-swarm PSO proposed in this work is generally inspired by the Socio-Cognitively Inspired PSO [22]. This algorithm will be presented in detail in Section V. The next couple of sections will focus on application domain -the scientific workflow scheduling, with the aim of developing the objective function that will be used in the multi-swarm PSO scheduling algorithm.…”

Section: B Multi-swarm Pso Approachesmentioning

confidence: 99%

“…Therefore, the multi-swarm PSO optimization algorithm tailored for this problem must be simple and characterized by a low computational complexity. In light of this understanding, this work adopts the Socio-Cognitively Inspired PSO [22] due to its apparent simplicity. Furthermore, in this approach, the swarms or species are evolved differently from each other.…”

Section: B the Proposed Ms-pso For Scientific Workflow Schedulingmentioning

confidence: 99%

“…The species in each swarm has its own algorithm for calculating the velocity. In stead of using the neighborhood's best position in the third term as in [22], entire swarm's best position is used. Therefore, at every k-th iteration, the elements of velocity v s i of the i-th particle in the s-th swarm are updated by using…”

Section: B the Proposed Ms-pso For Scientific Workflow Schedulingmentioning

confidence: 99%

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Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

Subramoney

Nyirenda

2022

IEEE Access

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Scientific workflow scheduling is a well-known problem that involves the allocation of workflow tasks to particular computational resources. The generation of optimal solutions to reduce runtime, cost, and energy consumption, as well as ensuring proper load balancing, remains a major challenge. Therefore, this work presents a Multi-Swarm based Particle Swarm Optimization (MS-PSO) algorithm to improve the scheduling of scientific workflows in cloud-fog environments. In particular, MS-PSO seeks to address the canonical PSO's problem of premature convergence, which leads it to suboptimal solutions. In MS-PSO, particles are divided into several swarms, with each swarm having its own cognitive and social learning coefficients. This work also develops a weighted sum objective function for the workflow scheduling problem, based on four objectives: makespan, cost, energy and load balancing for cloud and fog tiers. The FogWorkflowSim Toolkit is used in the evaluation process, with the objectives serving performance metrics. The MS-PSO approach is compared with the canonical PSO, Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO. The following scientific workflows are used in the simulations: Montage, Cybershake, Epigenomics, LIGO and SIPHT. MS-PSO outperforms the canonical PSO on all scientific workflows and under all performance metrics. It competes fairly well against the other approaches and it is more stable and reliable. It only ranks second to PSO, in terms of execution time. In future, multiple species, incorporating population update mechanisms from several algorithmic frameworks (MS-PSO, DE, GA), will be used for scientific workflow scheduling. Hybdridization of the realized algorithm with dynamic approaches will also be investigated.

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Section: B Multi-swarm Pso Approachesmentioning

confidence: 99%

Section: B Multi-swarm Pso Approachesmentioning

confidence: 99%

Section: B the Proposed Ms-pso For Scientific Workflow Schedulingmentioning

confidence: 99%

Section: B the Proposed Ms-pso For Scientific Workflow Schedulingmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

Subramoney

Nyirenda

2022

IEEE Access

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“…We have conducted a number of experiments with the application of this socio-cognitive ACO approach for solving the TSP (Traveling Salesman Problem) and adapting PSO (Particle Swarm Optimization) algorithms in a similar way (thus, creating socio-cognitive PSO). PSO was applied to the problem of global optimization in the continuous domain [1].…”

Section: Introductionmentioning

confidence: 99%

Emergence of Population Structure in Socio-Cognitively Inspired Ant Colony Optimization

Byrski

Świderska

Łasisz

et al. 2018

csci

Self Cite

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A metaheuristic proposed by us recently, Ant Colony Optimization (ACO) hybridized with socio-cognitive inspirations, turned out to generate interesting results when compared to classic ACO. Even though it does not always find better solutions to the considered problems, it usually finds sub-optimal solutions. Moreover, instead of a trial-and-error approach to configure the parameters of the ant species in the population, the actual structure of the population emerges from a predefined species-to-species ant migration strategies in our approach. Experimental results of our approach are compared to classic ACO and selected socio-cognitive versions of this algorithm.

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