Hybrid particle swarm optimization with simulated annealing

Pan, Xiuqin; Xue, Limiao; Lu, Yong; Sun, Na

doi:10.1007/s11042-018-6602-4

Cited by 55 publications

(20 citation statements)

References 10 publications

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“…Simulated Annealing (SA) is one of the most efficient and well‐developed local search algorithms. Developed in the early 80s by Kirkpatrick et al, SA is a simple, robust and more generalised type of optimisation algorithm 44 . The algorithm is analogous to a process in metallurgy.…”

Section: Fem‐based Analysis Of Vibration and Acoustic Noisementioning

confidence: 99%

Investigation on magnetostrictive behaviour of a converter transformer influenced by dominant harmonics: AFEMandANNbased approach

Yadav

Mehta

2021

Int Trans Electr Energ Syst

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Summary The introduction of harmonics to the HVDC system is imminent, which affects the components of the power system from varied perspectives. The present work emphasises analysing the behaviour of the converter transformer concerning the harmonics in the input. The process involves designing a stepped configuration of a 240‐MVA converter transformer using the finite element method (FEM). The designed model is analysed as per its structural dynamics to obtain its vibrational response for different ranges over the frequency spectrum. Furthermore, the module of acoustics and thermal analysis is coupled to the vibrational model. The coupled model serves as an efficient tool in analysing the impact of different orders of harmonics on the vibrational, acoustic and thermal performance of the core of the converter transformer. The computational effort taken in solving the coupled field problem is simplified by designing a neural network model and the prediction efficacy of the network is enhanced by using a Hybrid of Particle Swarm Optimisation and Gravitational Search Algorithm (HPSOGWO) as tuner of the internal parameters of the neural network. The significance of the designed model is also examined against other well‐versed hybrid algorithms in the literature in terms of various statistical indices.

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Section: Fem‐based Analysis Of Vibration and Acoustic Noisementioning

confidence: 99%

Investigation on magnetostrictive behaviour of a converter transformer influenced by dominant harmonics: AFEMandANNbased approach

Yadav

Mehta

2021

Int Trans Electr Energ Syst

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“…In this work, particle swarm optimization (PSO) has been used to assist with parameter optimization. PSO is well acknowledged as a method for undertaking parameter optimization methods for problems such as this [13]. PSO can undertake fast global searching which can be easily applied without complex parameter tuning [13].…”

Section: Novel Optimal Control Strategy and Implementationmentioning

confidence: 99%

“…PSO is well acknowledged as a method for undertaking parameter optimization methods for problems such as this [13]. PSO can undertake fast global searching which can be easily applied without complex parameter tuning [13]. The mode switch thresholds and EF values in ECMS are all optimized according to the provided driving cycle data.…”

Section: Novel Optimal Control Strategy and Implementationmentioning

confidence: 99%

Optimal Control Strategy for the Next Generation Range Extended Electric Bus

Zhang¹,

Murtagh²,

Early³

et al. 2020

SAE Technical Paper Series

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Electric and Hybrid-Electric buses have become a major vehicle platform for demonstrating the advantages and capabilities of electrification in heavy duty vehicles. This type of vehicle can be powered from several different sources that each have several unique operating characteristics and performance requirements that necessitate novel solutions. In this paper, a novel optimal control strategy based on the next generation range-extended electric bus (REEB) has been developed. Control strategies play an essential role in realizing the full potential of electric buses and through careful implementation can increase their effectiveness at displacing conventional internal-combustion powered buses and thus, reducing global fuel consumption and emissions. Initially, a control-oriented powertrain model was developed in Matlab/Simulink. A new control strategy was devised for a series-hybrid baseline vehicle based on an equivalent consumption minimization strategy (ECMS) to govern the powertrain when operating in different modes. Due to the high impact the drive cycle has on the equivalent factor (EF) in an ECMS, an offline optimization process is performed to further increase the effect of the ECMS on various driving routes. Particle swarm optimization (PSO) was applied to the offline control model, generating the most appropriate EF values for different driving routes. The optimization of the EF values, the implementation of the new control strategy and the control execution process are described here in detail. Finally, the proposed control strategy is demonstrated in the simulation environment by analysing its performance with those of a simple rule-based strategy and a dynamic programming based global optimisation strategy. Comparison of results suggests the superior performance of the proposed method in fuel economy optimization versus the rule-based control method and the similarities to the dynamic programming based control method.

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“…Algorithms include particle such as PSO, ACO, Gray wolf optimization (GWO), artificial bee colony (ABC), owl optimization algorithm (OOA), Falcon optimization algorithm (FOA), cuckoo search algorithm (CSA), and firefly algorithm (FA). Many researchers around the world have been benefited from the diversity in swarm-based algorithms, which are applied to solve complex optimization problems in various fields such as test scheduling problems, 23,24 engineering optimization problems, 10,11,[25][26][27][28] heat exchangers problems, [29][30][31] neural network parameter optimization, 32,33 health-care, 34,35 real-time object tracking, 36,37 protein detection, 38,39 task scheduling in cloud computing, 40,41 and clustering for wireless sensor networks. 42,43 The third category of algorithms that use physical or chemical systems, typically simulate physical phenomena occurring in nature like Newton's gravitational law, quantum mechanics, and universe theory.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The most popular techniques have elicited considerable attention in the search for a solution to accommodate concerns regarding optimization. The most popular individual‐based algorithms are hill climbing and simulated annealing (SA), whereas the most popular population‐based algorithms are particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) . Population‐based metaheuristics have some benefits over individual‐based algorithms because they have a high capability to explore and can reduce the probability to stick in local optima and cover all promising search space for information exchange between populations …”

Section: Introductionmentioning

confidence: 99%

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

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Hybrid particle swarm optimization with simulated annealing

Cited by 55 publications

References 10 publications

Investigation on magnetostrictive behaviour of a converter transformer influenced by dominant harmonics: AFEMandANNbased approach

Investigation on magnetostrictive behaviour of a converter transformer influenced by dominant harmonics: AFEMandANNbased approach

Optimal Control Strategy for the Next Generation Range Extended Electric Bus

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

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