Performance analysis and feasibility study of ant colony optimization, particle swarm optimization and cuckoo search algorithms for inverse heat transfer problems

Udayraj,; Mulani, Konica; Talukdar, Prabal; Das, Apurba; Alagirusamy, R.

doi:10.1016/j.ijheatmasstransfer.2015.05.015

Cited by 82 publications

(10 citation statements)

References 74 publications

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“…In this section, we propose an online distributed cooperative control approach for multiple high-speed train trajectory planning based on ACO algorithm, which was proposed and initially developed by Marco Dorigo and colleagues in early nineties based on the foraging behavior and concept of exploitation of the shortest path by the ants (Dorigo and Sttzle, 1999;Dorigo and Blum, 2005;Udayraj et al, 2015). The ACO algorithm has been adopted broadly in the railway field to solve the train regulation problem (Sama' et al, 2016;Fan et al, 2012) and also the train trajectory planning problem (Ke et al, 2011).…”

Section: Online Distributed Cooperative Optimization Algorithmmentioning

confidence: 99%

Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Xiao

Cai

Ning

et al. 2016

Transportation Research Part C: Emerging Technologies

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Section: Online Distributed Cooperative Optimization Algorithmmentioning

confidence: 99%

Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Xiao

Cai

Ning

et al. 2016

Transportation Research Part C: Emerging Technologies

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“…Peng et al [25] developed an improved version of PSO to optimize the structural dimensions of the pin-fin heat exchanger and found the optimum weight and overall cost under certain constraints. Alagirusamy et al [26] applied PSO to find out unknown boundary heat flux for conduction, convection, and coupled conduction-radiation problems. None of the work has been reported by researchers yet to optimize the S-shaped duct using a PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

Sharma

Baloni

2020

SN Appl. Sci.

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A high-bypass turbofan engine transfers air from low-to the high-pressure compressor through an S-shaped transition duct. Minimization of the total pressure loss and maximization of uniform flow are key factors to ensure the maximum performance of the S-shaped transition duct. The conventional design approach is time-consuming and does not guarantee an optimal solution. Hence, the present article is based on the application of optimization for the S-shaped compressor transition duct. The optimization is carried out on the basis of shape parameters to minimize objectives namely pressure loss coefficient and non-uniformity with the reduction of length of the S-shaped transition duct. The 2-D axisymmetric approach of computational fluid dynamics is used as a design tool for performance evaluation with optimization techniques. The simulation model is validated with the available experimental results of the literature. The correlation between the response and independent variables is established with the help of the second-order polynomial response surface methodology. Further, individual optimization is carried out for single-objective consideration using particle swarm optimization and gray wolf optimization algorithms. As the results of single-objective optimization depict the conflicts between both objectives, later optimization using multi-objective particle swarm optimization and multi-objective gray wolf optimization algorithms is carried out. The performance metrics are obtained and compared. The 'TOPSIS' method is applied to get best solution out of multiple solutions. The optimized duct reduced pressure loss coefficient and non-uniformity index by 28.14% and 43.33% with a reduction of 6.37% of length compared to baseline S-shaped duct.

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“…PSO is good at global optimization, whereas it has the disadvantages of easily trapping in a local optimum [17]. ACO finds better solutions of effective feedback and distributed computation, although it has shortcomings of long-searching time and an initial lack of pheromone [18]. By fusing these two algorithm, we can improve the convergence rate and avoid trapping into local optimum [19].…”

Section: Introductionmentioning

confidence: 99%

An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

Lü

Wang

2019

Applied Sciences

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Integrating a partnership with potentially stronger suppliers is widely acknowledged as a contributor to the organizational competitiveness of a supply chain. This paper proposes an event-based model which lists the events related with all phases of cooperation with partners and puts events into a dynamic supply chain network in order to understand factors that affect supply chain partnership integration. We develop a multi-objective supply chain partnership integration problem by maximizing trustworthiness, supplier service, qualified products rate and minimizing cost, and then, apply a hybrid algorithm (PSACO) with particle swarm optimization (PSO) and ant colony optimization (ACO) that aims to efficiently solve the problem. It combines the advantages of PSO with reliable global searching capability and ACO with great evolutionary ability and positive feedback. By using the actual data from 1688.com, experimental studies are carried out. The parameter optimizing of the hybrid algorithm is firstly deployed and then we compare the problem solution results of PSACO with the original PSO, ACO. By studying the partnership integration results and implementing analysis of variance (ANOVA) analysis, it shows that the event based model with PSACO approach has validity and superiority over PSO and ACO, and can be served as a tool of decision making for supply chain coordination management in business.

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Performance analysis and feasibility study of ant colony optimization, particle swarm optimization and cuckoo search algorithms for inverse heat transfer problems

Cited by 82 publications

References 74 publications

Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

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