Impact of Controlling Parameters on the Performance of MOPSO Algorithm

Rajani, .; Kumar, D. Nagesh; Kumar, Vijay

doi:10.1016/j.procs.2020.03.261

Cited by 20 publications

(5 citation statements)

References 9 publications

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“…Since the objective value for the f ENS is zero in all the Pareto optimal solutions, the optimization process is modified to find non-dominated solutions for the problem formulated in (14). The EVs' full charge requirement is then considered as a constraint in the problem.…”

Section: B Resultsmentioning

confidence: 99%

“…The performance of the proposed multi-objective optimization method is investigated using a 449-node distribution network, including medium voltage (MV) and low voltage (LV) feeders. The quality of the Pareto optimal solutions was analyzed by comparing the domination percentage based on C-index, spacing metric (SM), computational load, and hypervolume (HV) measurements to other well-known methods such as the multiobjective grey wolf optimizer (MOGWO) [13] and multiobjective particle swarm optimization (MOPSO) [14] algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective Advanced Grey Wolf optimization Framework for Smart Charging Scheduling of EVs in Distribution Grids

Ahmadi

Arias

Hoogsteen

et al. 2022

2022 57th International Universities Power Engineering Conference (UPEC)

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This paper proposes a multi-objective optimization technique for scheduling the charging of electric vehicles (EVs) in electrical distribution systems (DSs). A multi-objective advanced grey wolf optimization algorithm (MOAGWO) is developed to find the Pareto optimal solutions that minimize the DS's operational costs, energy losses costs, voltage violations, and the energy not supplied to EV users using several scenarios. A 449-node system with 63% penetration of EVs is used to demonstrate the efficiency of the proposed method. The quality of the non-dominated optimal solutions found by MOAGWO are validated via a comparison analysis with other well-known methods such as the multiobjective grey wolf optimizer (MOGWO) and the multiobjective particle swarm optimization (MOPSO) algorithm, based on domination rate, spacing index, hypervolume index, and computational cost measurements. The Pareto solutions indicate that the smart charging coordination found by MOAGWO makes the techno-economic operation of the DS possible while satisfying energy-based goals of the EV users.

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Section: B Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective Advanced Grey Wolf optimization Framework for Smart Charging Scheduling of EVs in Distribution Grids

Ahmadi

Arias

Hoogsteen

et al. 2022

2022 57th International Universities Power Engineering Conference (UPEC)

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“…2. The related research on the flight parameters of MOPSO has been introduced in Rajani et al (2020). Generally, the larger ω and c 1 , and the smaller c 2 can boost the global exploration.…”

Section: Adaptive Flight Parameter Adjustmentmentioning

confidence: 99%

“…It is usually difficult to design a reasonable experiment to explain the computational complexity or computational time of the algorithms. Many methods have been proposed in Qiao et al (2020), Hu and Yen (2015), and Rajani et al (2020), and a general and simple one is adopted in this paper, that is, the running time of the algorithms under the same environmental platform and the same maximum number of iterations. On the basis of comparing the convergence and diversity, the running time of each algorithm is measured simultaneously to fairly demonstrate the computational complexity of the proposed tssAMOPSO algorithm.…”

Section: Comparison Of Computational Timementioning

confidence: 99%

Adaptive Multi-Objective Particle Swarm Optimization Using Three-Stage Strategy

Huang¹,

Zhang²

2021

Preprint

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It is one of the crucial problems in solving multi-objective problems (MOPs) that balance the convergence and diversity of the algorithm to obtain an outstanding Pareto optimal solution set. In order to elevate the performance further and improve the optimization efficiency of multi-objective particle swarm optimization (MOPSO), a novel adaptive MOPSO using a three-stage strategy (tssAMOPSO) is proposed in this paper, which can effectively balance the exploration and exploitation of the population and facilitate the convergence and diversity of MOPSO. Firstly, an adaptive flight parameter adjustment, formulated by the convergence contribution of nondominated solutions, can ameliorate the convergence and diversity of the algorithm enormously. Secondly, the population carries out the three-stage strategy of optimization in each iteration, namely adaptive optimization, decomposition, and Gaussian attenuation mutation. The three-stage strategy remarkably promotes the diversity and efficiency of the optimization process. Moreover, the convergence of three-stage optimization strategy is analyzed. Then, memory interval is equipped with particles to record the recent positions, which vastly improves the reliability of personal best selection. In the maintenance of external archive, the proposed fusion index can enhance the quality of nondominated solutions directly. Finally, comparative experiments are designed by a series of benchmark instances to verify the performance of tssAMOPSO. Experimental results show that the proposed algorithm achieves admirable performance compared with other contrast algorithms.

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“…In addition, a few scholars have improved the MOPSOs from the aspect of parameter setting to make the MOPSO more optimized [ 21 ]. In view of the effective analysis of the abovementioned existing algorithms, this article combined with the cosine distance update mechanism and the meshing strategy.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy

Liu

Shi

et al. 2021

Computational Intelligence and Neuroscience

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The optimization problems are taking place at all times in actual lives. They are divided into single objective problems and multiobjective problems. Single objective optimization has only one objective function, while multiobjective optimization has multiple objective functions that generate the Pareto set. Therefore, to solve multiobjective problems is a challenging task. A multiobjective particle swarm optimization, which combined cosine distance measurement mechanism and novel game strategy, has been proposed in this article. The cosine distance measurement mechanism was adopted to update Pareto optimal set in the external archive. At the same time, the candidate set was established so that Pareto optimal set deleted from the external archive could be effectively replaced, which helped to maintain the size of the external archive and improved the convergence and diversity of the swarm. In order to strengthen the selection pressure of leader, this article combined with the game update mechanism, and a global leader selection strategy that integrates the game strategy including the cosine distance mechanism was proposed. In addition, mutation was used to maintain the diversity of the swarm and prevent the swarm from prematurely converging to the true Pareto front. The performance of the proposed competitive multiobjective particle swarm optimizer was verified by benchmark comparisons with several state-of-the-art multiobjective optimizer, including seven multiobjective particle swarm optimization algorithms and seven multiobjective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of optimization quality.

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Impact of Controlling Parameters on the Performance of MOPSO Algorithm

Cited by 20 publications

References 9 publications

Multi-objective Advanced Grey Wolf optimization Framework for Smart Charging Scheduling of EVs in Distribution Grids

Multi-objective Advanced Grey Wolf optimization Framework for Smart Charging Scheduling of EVs in Distribution Grids

Adaptive Multi-Objective Particle Swarm Optimization Using Three-Stage Strategy

Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy

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