Large-scale multiobjective optimization with adaptive competitive swarm optimizer and inverse modeling

Ge, Yuanyuan; Chen, Debao; Zou, Feng; Fu, Minglan; Ge, Fangzhen

doi:10.1016/j.ins.2022.07.018

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Large-scale MOPs constitute the primary research domain of CSO [24], leading to the proposal of various improved versions of CSO. Mohapatra designed a tri-competitive schema-based CSO to enhance exploration [25][26][27], while Ge introduced inverse modeling to update winners and accelerate the convergence of CSO [28]. Liu devised three distinct competitive schemas to improve the diversity of CSO [29], and Qi incorporated a neighborhood search strategy to enhance CSO [30].…”

Section: Competitive Swarm Optimizermentioning

confidence: 99%

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Zhang,

Li,

et al. 2024

Complex Intell. Syst.

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This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize the maximum finish time and total energy consumption. To accomplish this, we introduce a learning-based cooperative and competitive multi-objective optimization method, which we refer to as LCCMO. We begin by presenting a multi-rule cooperative initialization approach to create a population that combines strong convergence and diversity. This diverse population forms the foundation for our optimization process. Next, we develop a multi-level cooperative global search strategy that explores effective genes within solutions from different angles and sub-problems. This approach enhances our search for optimal solutions. Moreover, we design a competition and cooperation strategy for different populations to expedite convergence. This strategy encourages the exchange of information and ideas among diverse populations, thereby accelerating our progress. We also introduce a multi-operator cooperative local search technique, which investigates elite solutions from various directions, leading to improved convergence and diversity. In addition, we integrate Q-learning into our competitive swarm optimizer to explore different regions of the objective space, enhancing the diversity of the elite archive. Q-learning guides the selection of operators within the small-size population, contributing to more efficient optimization. To evaluate the effectiveness of LCCMO, we conduct numerical experiments on 20 instances. The experimental results unequivocally demonstrate that LCCMO outperforms six state-of-the-art algorithms. This underscores the potential of our learning and knowledge-driven evolutionary framework in enhancing performance and autonomy when it comes to solving EADHWS.

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Section: Competitive Swarm Optimizermentioning

confidence: 99%

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Zhang,

Li,

et al. 2024

Complex Intell. Syst.

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“…When the size of the nondominant solution set is larger than γ , the new nondominant solution set is determined through the environmental selection of the RVEA algorithm. The study of Pareto optimal solution set size constraints in adaptive competitive multi-objective swarm algorithm based on inverse modeling [32] shows that the effect is best when γ = N/20. Algorithm 3 shows the details of the environmental selection strategy.…”

Section: Environmental Selection Strategymentioning

confidence: 99%

Design Optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence Algorithm

Wang,

Pan,

Niu

2023

CMC

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The permanent magnet eddy current coupler (PMEC) solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems. It provides torque to the load and generates heat and losses, reducing its energy transfer efficiency. This issue has become an obstacle for PMEC to develop toward a higher power. This paper aims to improve the overall performance of PMEC through multi-objective optimization methods. Firstly, a PMEC modeling method based on the Levenberg-Marquardt back propagation (LMBP) neural network is proposed, aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC. Then, a novel competition mechanism-based multi-objective particle swarm optimization algorithm (NCMOPSO) is proposed to find the optimal structural parameters of PMEC. Chaotic search and mutation strategies are used to improve the original algorithm, which improves the shortcomings of multi-objective particle swarm optimization (MOPSO), which is too fast to converge into a global optimum, and balances the convergence and diversity of the algorithm. In order to verify the superiority and applicability of the proposed algorithm, it is compared with several popular multi-objective optimization algorithms. Applying them to the optimization model of PMEC, the results show that the proposed algorithm has better comprehensive performance. Finally, a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results. Compared with the prototype, the optimized PMEC has reduced eddy current losses by 1.7812 kW, increased output torque by 658.5 N•m, and decreased costs by 13%, improving energy transfer efficiency.

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“…Tian et al [2] used a competitive group optimizer to solve LSMOPs. Recently, novel search strategies, such as generative adversarial networks [34], self-exploratory competitive swarm optimization [35], adaptive competitive swarm optimization with inverse modeling [36], and probabilistic prediction models [37] were introduced to solve LSMOPs.…”

Section: Sampled Decision Variablesmentioning

confidence: 99%

Analysis on Key Technologies of Digital Protection of Intangible Cultural Heritage Archives

Zhang

2021

2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)

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The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world applications in engineering fields can be modeled as LSMOPs; simultaneously, engineering applications require insensitivity in performance. This requirement usually means that the results from the algorithm runs should not only be good for every run in terms of performance but also that the performance of multiple runs should not fluctuate too much, i.e., the algorithm shows good insensitivity. Considering that substantial computational resources are requested for each run, it is essential to improve upon the performance of the large-scale multiobjective optimization algorithm, as well as the insensitivity of the algorithm. However, existing large-scale multiobjective optimization algorithms solely focus on improving the performance of the algorithms, leaving the insensitivity characteristics unattended. In this work, we propose an evolutionary algorithm for solving LSMOPs based on Monte Carlo tree search, the socalled LMMOCTS, which aims to improve the performance and insensitivity for large-scale multiobjective optimization problems. The proposed method samples the decision variables to construct new nodes on the Monte Carlo tree for optimization and evaluation. It selects nodes with good evaluation for further search to reduce the performance sensitivity caused by largescale decision variables. We compare the proposed algorithm with several state-of-the-art designs on different benchmark functions. We also propose two metrics to measure the sensitivity of the algorithm. The experimental results confirm the effectiveness and performance insensitivity of the proposed design for solving largescale multiobjective optimization problems.

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Large-scale multiobjective optimization with adaptive competitive swarm optimizer and inverse modeling

Cited by 11 publications

References 25 publications

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Design Optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence Algorithm

Analysis on Key Technologies of Digital Protection of Intangible Cultural Heritage Archives

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