Particle Swarm Optimization Applications to Mechanical Engineering- A Review

Kulkarni, Ninad; Patekar, Sujata; Bhoskar, Trupti; Kulkarni, Omkar; Kakandikar, Ganesh M.; Nandedkar, Vilas M.

doi:10.1016/j.matpr.2015.07.223

Cited by 58 publications

(20 citation statements)

References 9 publications

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“…This optimization algorithm has been applied in many engineering applications, such as gear train design, process parameter optimization in casting, power generation scheduling, etc. [45,46]. The population-based optimization has been adopted for the stochastic optimization in this study because it offers mathematical flexibility and computational efficiency to incorporate uncertainties.…”

Section: Stochastic Optimization Algorithmmentioning

confidence: 99%

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Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use

Tran

Smith

2019

Energies

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Stochastic optimization of a district energy system (DES) is investigated with renewable energy systems integration and uncertainty analysis to meet all three major types of energy consumption: electricity, heating, and cooling. A district of buildings on the campus of the University of Utah is used as a case study for the analysis. The proposed DES incorporates solar photovoltaics (PV) and wind turbines for power generation along with using the existing electrical grid. A combined heat and power (CHP) system provides the DES with power generation and thermal energy for heating. Natural gas boilers supply the remaining heating demand and electricity is used to run all of the cooling equipment. A Monte Carlo study is used to analyze the stochastic power generation from the renewable energy resources in the DES. The optimization of the DES is performed with the Particle Swarm Optimization (PSO) algorithm based on a day-ahead model. The objective of the optimization is to minimize the operating cost of the DES. The results of the study suggest that the proposed DES can achieve operating cost reductions (approximately 10% reduction with respect to the current system). The uncertainty of energy loads and power generation from renewable energy resources heavily affects the operating cost. The statistical approach shows the potential to identify probable operating costs at different time periods, which can be useful for facility managers to evaluate the operating costs of their DES.

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Section: Stochastic Optimization Algorithmmentioning

confidence: 99%

“…], r 1 and r 2 are random numbers ⊂ (0,1), j is the jth particle, and k is the kth iteration [45]. The three terms in Equations (21) represent inertial, cognitive, and social components, respectively.…”

Section: Stochastic Optimization Algorithmmentioning

confidence: 99%

Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use

Tran

Smith

2019

Energies

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“…Among them, PSOs have been greatly adopted during recent years in practical domains [28,29]. The Particle Swarm Optimization (PSO) is a population-based optimization algorithm based on the benchmarking of social interactions such as bird ocking or sh school.…”

Section: Improved Particle Swarm Optimization (Ipso)mentioning

confidence: 99%

Integrated production and distribution scheduling for perishable products

Marandi

Zegordi

2017

Scientia Iranica

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Abstract. This study is concerned with how the quality of perishable products can be improved by shortening the time interval between production and distribution. Since special types of food, such as dairy products, decay fast, the Integration of Production and Distribution Scheduling (IPDS), is investigated. This article deals with a variation of IPDS that contains a short shelf life product; hence, there is no inventory of the product in the process. Once a speci c amount of the product is produced, it must be transported with the least transportation time directly to various customer positions within its limited lifespans to minimize the delivery and tardy costs required to complete producing and distributing of the product to satisfy the demand of customers within the limited deadline. After developing a mixed-integer nonlinear programming model of the problem, because it is NP-hard, an Improved Particle Swarm Optimization (IPSO) is proposed. IPSO performance is compared with commercial optimization software for small-size and moderate-size problems. For large-size ones, it is compared with the genetic algorithm existing in the literature. Computational experiments show the e ciency and e ectiveness of the proposed IPSO in terms of both the quality of the solution and the time of achieving the best solution.

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“…Nature-inspired and Bio-inspired algorithms nowadays play a very vital role in the optimization of the process parameters or in solving real-life problems. There are many such algorithms which can be listed as Genetic algorithm (GA) [10], Particle Swarm Optimization (PSO) [11], Grey Wolf Algorithm (GWA) [12], Cuckoo Search Algorithm (CS) [13], Bat Algorithm (BA) [14], Salp Swarm Algorithm (SSA) [15], Firefly Algorithm (FA) [16], Flower Pollination Algorithm (FPA) [17] etc. also there are algorithms like Socio inspired algorithms which are on the social behaviour of human beings such as Cohort Intelligence Algorithm (CI) [18], Ideology Algorithm (IA) [19] etc.…”

Section: Introductionmentioning

confidence: 99%

Parameter optimization of AISI 316 austenitic stainless steel for surface roughness by Grasshopper optimization algorithm

Kulkarni¹,

Jawade²,

Kakandikar³

2021

J., mech. eng. autom., control. syst.

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This article describes the optimization of processing parameters for the surface roughness of AISI316 austenitic stainless steel. While experimenting, parameters in the process like feed rate (fd), speed (vc), and depth of cut (DoC) were used to study the outcome on the surface roughness (Ra) of the workpiece. The experiment was carried out using the design of experiments (DOE) on a computer numerical control (CNC) lathe. The surface roughness is tested for three conditions i.e. Dry, Wet, and cryogenic conditions after the turning process. Samples are step turned on CNC Lathe for all three conditions with a set of experiments designed. The response surface methodology is implemented, and mathematical models are built for all three conditions. The nature-inspired algorithm is the best way to get the optimal value. For the discussed problem in the paper, nature-inspired techniques are used for obtaining the optimum parameter values to get minimum surface roughness for all set conditions. The Grasshopper optimization algorithm (GOA) is the technique that is the most effective method for real-life applications. In this research, GOA is used to get optimum values for the surface roughness (Ra) at Dry, Wet and cryogenic conditions. Finally, results are compared, and it's observed that the values obtained from GOA are minimum in surface roughness value.

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Particle Swarm Optimization Applications to Mechanical Engineering- A Review

Cited by 58 publications

References 9 publications

Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use

Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use

Integrated production and distribution scheduling for perishable products

Parameter optimization of AISI 316 austenitic stainless steel for surface roughness by Grasshopper optimization algorithm

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