Particle Swarm Optimization with Applications最新文献

Performance Comparison of PSO and Its New Variants in the Context of VLSI Global Routing VLSI全局路由环境下PSO及其新变体的性能比较

Particle Swarm Optimization with Applications Pub Date : 2018-05-30 DOI: 10.5772/INTECHOPEN.72811

Subhrapratim Nath, J. Sing, S. Sarkar

{"title":"Performance Comparison of PSO and Its New Variants in the Context of VLSI Global Routing","authors":"Subhrapratim Nath, J. Sing, S. Sarkar","doi":"10.5772/INTECHOPEN.72811","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.72811","url":null,"abstract":"Substantial reduction of gate delay occurred in recent times owing to radical decrement of transistor size. The interconnect length and delay are accordingly increased owing to the exponential escalation of packaging density with additional transistors being fabri- cated on the same chip area. The function of VLSI routing that seems to be more defying to the scholars, is categorized in global routing and detailed routing phase. In global routing phase, the prevalent method to lessen the wire length for reducing interconnect delay is to adjust the cost of the Steiner tree, devised by the terminal nodes to be interconnected. Nevertheless, Steiner tree problem is a NP-complete problem in classical graph theory where meta-heuristics might impart beneficial elucidations. Particle swarm optimization (PSO) is a robust algorithm concerning VLSI routing field. This chapter is regarding the proposal of a self-adaptive mechanism for monitoring acceler- ation coefficient of PSO and evaluating its functionalities with the existing acceleration coefficient controlled PSO in numerous allocation topologies of terminal nodes within definite VLSI layout. The outcomes of PSO variant with constriction factor in context to VLSI route reduction ability and robustness are also inspected. Additionally, a new effort in adapting the PSO with embracement of genetic algorithm is established.","PeriodicalId":365322,"journal":{"name":"Particle Swarm Optimization with Applications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130290051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Introductory Chapter: Swarm Intelligence and Particle Swarm Optimization 导论章:群体智能和粒子群优化

Particle Swarm Optimization with Applications Pub Date : 2018-05-30 DOI: 10.5772/INTECHOPEN.74076

P. Erdoğmuş

引用次数: 5

Stochastic Greedy-Based Particle Swarm Optimization for Workflow Application in Grid 基于随机贪婪的粒子群优化在网格中工作流中的应用

Particle Swarm Optimization with Applications Pub Date : 2018-02-01 DOI: 10.5772/INTECHOPEN.73587

Ruey-Maw Chen, Yin-mou Shen

{"title":"Stochastic Greedy-Based Particle Swarm Optimization for Workflow Application in Grid","authors":"Ruey-Maw Chen, Yin-mou Shen","doi":"10.5772/INTECHOPEN.73587","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.73587","url":null,"abstract":"The workflow application is a common grid application. The objective of a workflow application is to complete all the tasks within the shortest time, i.e., minimal makespan. A job scheduler with a high-efficient scheduling algorithm is required to solve workflow scheduling based on grid information. Scheduling problems are NP-complete problems, which have been well solved by metaheuristic algorithms. To attain effective solutions to workflow application, an algorithm named the stochastic greedy PSO (SGPSO) is proposed to solve workflow scheduling; a new velocity update rule based on stochastic greedy is suggested. Restated, a stochastic greedy-driven search guidance is provided to particles. Meanwhile, a stochastic greedy probability (SGP) parameter is designed to help control whether the search behavior of particles is exploitation or exploration to improve search efficiency. The advantages of the proposed scheme are retaining exploration capa- bility during a search, reducing complexity and computation time, and easy to implement. Retaining exploration capability during a search prevents particles from getting trapped on local optimums. Additionally, the diversity of the proposed SGPSO is verified and analyzed. The experimental results demonstrate that the SGPSO proposed can effectively solve workflow class problems encountered in the grid environment.","PeriodicalId":365322,"journal":{"name":"Particle Swarm Optimization with Applications","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129376236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Particle Swarm Optimization Algorithm with a Bio-Inspired Aging Model 基于仿生老化模型的粒子群优化算法

Particle Swarm Optimization with Applications Pub Date : 2017-12-20 DOI: 10.5772/INTECHOPEN.71791

Eduardo Rangel-Carrillo, E. Hernández-Vargas, N. Arana-Daniel, C. López-Franco, A. Alanis

引用次数: 0

Solution of Combined Economic Emission Dispatch Problem with Valve-Point Effect Using Hybrid NSGA II-MOPSO 采用混合NSGA - mopso求解具有阀点效应的联合经济排放调度问题

Particle Swarm Optimization with Applications Pub Date : 2017-12-20 DOI: 10.5772/INTECHOPEN.72807

Arunachalam Sundaram

{"title":"Solution of Combined Economic Emission Dispatch Problem with Valve-Point Effect Using Hybrid NSGA II-MOPSO","authors":"Arunachalam Sundaram","doi":"10.5772/INTECHOPEN.72807","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.72807","url":null,"abstract":"This chapter formulates a multi-objective optimization problem to simultaneously mini- mize the objectives of fuel cost and emissions from the power plants to meet the power demand subject to linear and nonlinear system constraints. These conflicting objectives are formulated as a combined economic emission dispatch (CEED) problem. Various meta-heuristic optimization algorithms have been developed and successfully implemented to solve this complex, highly nonlinear, non-convex problem. To overcome the shortcomings of the evolutionary multi-objective algorithms like slow convergence to Pareto-optimal front, premature convergence, local trapping, it is very natural to think of integrating various algorithms to overcome the shortcomings. This chapter proposes a hybrid evolu- tionary multi-objective optimization framework using Non-Dominated Sorting Genetic Algorithm II and Multi-Objective Particle Swarm Optimization to solve the CEED prob- lem. The hybrid method along with the proposed constraint handling mechanism is able to balance the exploration and exploitation tasks. This hybrid method is tested on IEEE 30 bus system with quadratic cost function considering transmission loss and valve point effect. The Pareto front obtained using hybrid approach demonstrates that the approach converges to the true Pareto front, finds the diverse set of solutions along the Pareto front and confirms its potential to solve the CEED problem.","PeriodicalId":365322,"journal":{"name":"Particle Swarm Optimization with Applications","volume":"54 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131726662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Particle Swarm Optimization Solution for Power System Operation Problems 电力系统运行问题的粒子群优化解

Particle Swarm Optimization with Applications Pub Date : 2017-12-20 DOI: 10.5772/INTECHOPEN.72409

M. Kheshti, L. Ding

引用次数: 6