Optimization Design of Parameters with Hybrid Particle Swarm Optimization Algorithm in Multi-hole Extrusion Process

Chen, Wen-Jong; Chen, Dyi-Cheng; Su, Wen Cheng; Nian, Fung Ling

doi:10.1007/978-3-642-27296-7_44

Cited by 2 publications

(2 citation statements)

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“…The optimization problem is to minimize (1) by using different values of price penalty factors as given in (6)(7)(8)(9).…”

Section: A Minimization Of Objective Functionmentioning

confidence: 99%

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Multiarea Economic-Emission Dispatch Using Simplex Based Particle Swarm Optimization Method

Chopra,

Brar,

Dhillon

2018

AJES

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This paper introduces the hybridization of Particle Swarm Optimization (PSO) with Simplex Search Method (SSM) in the field of Multi Area Power Dispatch (MAPD) problem. The proposed simplex based particle swarm optimization (SPSO) helps in refining the global solution obtained using PSO, with local search through SSM. As in today’s power industry, the energy flow between the areas and fossil fuel emissions from the generating units are of major concern. So, the objective of MAPD is to reduce the overall generation cost of the areas along with the reduction of pollutants’ emissions also. The comparison with Single Area Power Dispatch (SAPD) system is done to understand the benefit of resolving the whole region into small areas. Due to the conflicting nature of both the objectives, Price Penalty Factor (PPF) method is used to convert the multiobjective problem into single objective optimization problem while satisfying its various equality and inequality constraints. The effectiveness of the proposed method is tested on a test system consisting four areas, twelve generator system under both MAPD and SAPD cases. These cases are compared with each other and with the available literature to show the robustness of the proposed method.

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“…The optimization problem is to minimize (1) by using different values of price penalty factors as given in (6)(7)(8)(9).…”

Section: A Minimization Of Objective Functionmentioning

confidence: 99%

“…In 1995, a stochastic optimization approach developed by Eberhart and Kennedy, motivated from the social behavior of bird flocking [9]. This study is based on the fact that during the movement, each swarm modifies its position as per its own observation also as per the observation of other swarms.…”

Section: Implementation Of Proposed Spso On Sapd/mapd Problemmentioning

confidence: 99%