Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization

Punnathanam, Varun; Kotecha, Prakash

doi:10.1016/j.enconman.2016.10.035

Cited by 37 publications

(15 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The YYPO optimizer is one of the recent MA inspired by the philosophy of balance among discordant concepts (Punnathanam and Kotecha, 2017). In YYPO, the variables of the tasks should be normalized inside interval (0, 1).…”

Section: The Yypo Algorithmmentioning

confidence: 99%

“…Then, the best point in the present ARC, if superior to P2, substitutes P2. Thus, the best two points will continuously survive sequential repetitions (Punnathanam and Kotecha, 2017). The R1 and R2 are updated as:…”

Section: Archive Stagementioning

confidence: 99%

“…In one-way strategy, simply one variable of each P is adjusted. In the D-way method, all variables of each P are updated (Punnathanam and Kotecha, 2017).…”

Section: Splitting Phasementioning

confidence: 99%

“…In 2017, a hybrid PSO-tabu search optimizer has been proposed to treat the hierarchical FL problem (Ou-Yang and Ansari, 2017). The Yin-Yang-pair optimization (YYPO) is one of the recent MA that mimics the philosophy of balance among discordant concepts (Punnathanam and Kotecha, 2017). This algorithm has shown an efficient performance in dealing with several optimization problems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Hybrid Yin-Yang-Pair-Particle Swarm Optimization Algorithm for Uncapacitated Warehouse Location Problems

Heidari¹,

Kazemizade²,

Hakimpour³

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Yin-Yang-pair optimization (YYPO) is one of the latest metaheuristic algorithms (MA) proposed in 2015 that tries to inspire the philosophy of balance between conflicting concepts. Particle swarm optimizer (PSO) is one of the first population-based MA inspired by social behaviors of birds. In spite of PSO, the YYPO is not a nature inspired optimizer. It has a low complexity and starts with only two initial positions and can produce more points with regard to the dimension of target problem. Due to unique advantages of these methodologies and to mitigate the immature convergence and local optima (LO) stagnation problems in PSO, in this work, a continuous hybrid strategy based on the behaviors of PSO and YYPO is proposed to attain the suboptimal solutions of uncapacitated warehouse location (UWL) problems. This efficient hierarchical PSO-based optimizer (PSOYPO) can improve the effectiveness of PSO on spatial optimization tasks such as the family of UWL problems. The performance of the proposed PSOYPO is verified according to some UWL benchmark cases. These test cases have been used in several works to evaluate the efficacy of different MA. Then, the PSOYPO is compared to the standard PSO, genetic algorithm (GA), harmony search (HS), modified HS (OBCHS), and evolutionary simulated annealing (ESA). The experimental results demonstrate that the PSOYPO can reveal a better or competitive efficacy compared to the PSO and other MA.

show abstract

Section: The Yypo Algorithmmentioning

confidence: 99%

Section: Archive Stagementioning

confidence: 99%

“…In one-way strategy, simply one variable of each P is adjusted. In the D-way method, all variables of each P are updated (Punnathanam and Kotecha, 2017).…”

Section: Splitting Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Hybrid Yin-Yang-Pair-Particle Swarm Optimization Algorithm for Uncapacitated Warehouse Location Problems

Heidari¹,

Kazemizade²,

Hakimpour³

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Most problems [1][2][3][4][5][6] in engineering and science are multi-objective optimization problems, and these objectives usually conflict with each other. The focus of academics and engineers is on finding an optimal solution for these problems.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity

2019

View full text Add to dashboard Cite

It is generally known that the balance between convergence and diversity is a key issue for solving multi-objective optimization problems. Thus, a chaotic multi-objective particle swarm optimization approach incorporating clone immunity (CICMOPSO) is proposed in this paper. First, points in a non-dominated solution set are mapped to a parallel-cell coordinate system. Then, the status of the particles is evaluated by the Pareto entropy and difference entropy. At the same time, the algorithm parameters are adjusted by feedback information. At the late stage of the algorithm, the local-search ability of the particle swarm still needs to be improved. Logistic mapping and the neighboring immune operator are used to maintain and change the external archive. Experimental test results show that the convergence and diversity of the algorithm are improved.

show abstract

A comprehensive review on modeling and performance optimization of Stirling engine

et al. 2020

View full text Add to dashboard Cite

Summary Modeling with optimization has become a ubiquitous practice in the field of Stirling engine. A plethora of studies in the literature is dedicated in developing a feasible optimized model that can precisely predict the performance of Stirling engine. Hence, the purpose of this article is to compile and expansively review the thermodynamic models and optimization efforts made in pursuit of performance enhancement of the Stirling engine. An extensive range of models available in the literature is painstakingly discussed. Likewise, a wide variety of available optimization techniques spanning from conventional experimental and univariate methods to more complex multiobjective optimization approach are critically reviewed. A comparative analysis of the models is carried out based on the accuracy of their predictability of the performance of Stirling engines. Results obtained from the models are validated through the experimental data of the GPU‐3 Stirling engine prototype. Several optimization techniques are investigated based on the effective and efficient optimization of operating and geometric parameters of the Stirling engine. The review concluded that the Comprehensive Polytropic Model of Stirling engine (CPMS) demonstrated better accuracy in comparison with other models. In addition, the multiobjective particle swarm optimization (MOPSO) technique was found to be effective and computationally efficient.

show abstract

Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization

Cited by 37 publications

References 60 publications

A New Hybrid Yin-Yang-Pair-Particle Swarm Optimization Algorithm for Uncapacitated Warehouse Location Problems

A New Hybrid Yin-Yang-Pair-Particle Swarm Optimization Algorithm for Uncapacitated Warehouse Location Problems

Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity

A comprehensive review on modeling and performance optimization of Stirling engine

Contact Info

Product

Resources

About