A Fast Multi-Objective Particle Swarm Optimization Algorithm Based on a New Archive Updating Mechanism

Alkebsi, Khalil; Du, Wenli

doi:10.1109/access.2020.3007846

Cited by 23 publications

(6 citation statements)

References 44 publications

(55 reference statements)

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“…In [18], Chen et al used the decomposition idea to transform MOPs into a single-objective optimization problem and proposed a new speed update mechanism. In addition to the MOPSO mentioned above based on crowding distance and decomposition, many representative MOPSOs have appeared in recent years, such as GMRMOPSO [2], MOPSO-ASFS [23], AMPSO/ESE [24], MOPSONN [25], and others. In particular, in MOPSONN, the author proposed an archive updating mechanism based on the nearest neighbor method using the competition mechanism.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective Particle Swarm Optimization Based on Ideal Distance

Wang

Liu

Zou

et al. 2022

Discrete Dynamics in Nature and Society

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Recently, multiobjective particle swarm optimization (MOPSO) has been widely used in science and engineering, however how to effectively improve the convergence and distribution of the algorithm has always been a hot research topic on multiobjective optimization problems (MOPs). To solve this problem, we propose a multiobjective particle swarm optimization based on the ideal distance (IDMOPSO). In IDMOPSO, the adaptive grid and ideal distance are used to optimize and improve the selection method of global learning samples and the size control strategy of the external archive, and the fine-tuning parameters are introduced to adjust particle flight in the swarm dynamically. Additionally, to prevent the algorithm from falling into a local optimum, the cosine factor is introduced to mutate the position of the particles during the exploitation and exploration process. Finally, IDMOPSO, several other popular MOPSOs and MOEAs were simulated on the benchmarks functions to test the performance of the proposed algorithm using IGD and HV indicators. The experimental results show that IDMOPSO has the better convergence, diversity, and excellent solution ability compared to the other algorithms.

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Section: Related Workmentioning

confidence: 99%

Multiobjective Particle Swarm Optimization Based on Ideal Distance

Wang

Liu

Zou

et al. 2022

Discrete Dynamics in Nature and Society

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“…MOAFSA. According to the ideas of multiobjective evolutionary algorithm and multiobjective particle swarm algorithm [23][24][25][26][27], to improve the basic artificial fish swarm algorithm to multiobjective artificial fish swarm algorithm, the following three issues need to be addressed. ( 1) Fast searching Pareto optimal solutions and constructing Pareto optimal solution set; (2) determining the moving direction of artificial fish under multiobjective conditions; and (3) maintaining and updating the bulletin board (external archives) of artificial fish swarm.…”

Section: Multiobjective Artificial Fish Swarm Algorithmmentioning

confidence: 99%

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

Yang

Liu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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The single-stand reversible cold rolling mill is important equipment in the production of steel strips. The rolling schedule is the core technological content in the strip production of the single-stand reversible cold rolling mill. The scientific rolling schedule is the fundamental guarantee for the production capacity of the rolling mill, product quality, accuracy, shape quality, energy saving, and consumption reduction. This paper takes the dynamic rolling process of single-stand cold rolling as the research object, the purposes of increasing production capacity, saving energy, and reducing consumption are achieved by optimizing the rolling schedule. Based on the study of the mechanism model and the analysis of a large number of field measured data, a slice of mathematical models of the rolling process suitable for engineering calculation are proposed, and a few objective functions suitable for the single-stand reversible cold rolling process are designed. On this basis, the artificial fish swarm algorithm is improved into a multiobjective optimization algorithm for the optimization of rolling schedule, and the optimal rolling load distribution scheme is obtained. Finally, the optimization method of rolling schedule proposed in this paper is applied to the actual rolling production. The results show that the proposed method can improve productivity and save energy compared with the empirical rolling schedule, and the feasibility and validity of the proposed algorithm are verified.

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“…where the range of x 1 and x 2 are from −4 to 4 in Equations ( 9) and ( 10). The range of x 1 , x 2 , and x 3 are from 0 to 1 in Equations ( 11) and (12). The SKMOO shows superior performance than existing algorithms.…”

Section: Verification Of Proposed Algorithmmentioning

confidence: 99%

Optimal Design of IPMSM for EV Using Subdivided Kriging Multi-Objective Optimization

et al. 2021

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In this paper, subdivided kriging multi-objective optimization (SKMOO) is proposed for the optimal design of interior permanent magnet synchronous motor (IPMSM). The SKMOO with surrogate kriging model can obtain a uniform and accurate pareto front set with a reduced computation cost compared to conventional algorithms which directly adds the solution in the objective function area. In other words, the proposed algorithm uses a kriging surrogate model, so it is possible to know which design variables have the value of the objective function on the blank space. Therefore, the solution can be added directly in the objective function area. In the SKMOO algorithm, a non-dominated sorting method is used to find the pareto front set and the fill blank method is applied to prevent premature convergence. In addition, the subdivided kriging grid is proposed to make a well-distributed and more precise pareto front set. Superior performance of the SKMOO is confirmed by compared conventional multi objective optimization (MOO) algorithms with test functions and are applied to the optimal design of IPMSM for electric vehicle.

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A Fast Multi-Objective Particle Swarm Optimization Algorithm Based on a New Archive Updating Mechanism

Cited by 23 publications

References 44 publications

Multiobjective Particle Swarm Optimization Based on Ideal Distance

Multiobjective Particle Swarm Optimization Based on Ideal Distance

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

Optimal Design of IPMSM for EV Using Subdivided Kriging Multi-Objective Optimization

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