Multimodal multi-objective optimization: Comparative study of the state-of-the-art

Li, Wenhua; Zhang, Tao; Wang, Rui; Huang, Shengjun; Liang, Jing

doi:10.1016/j.swevo.2023.101253

Cited by 49 publications

(11 citation statements)

References 36 publications

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“…In recent years, various multi-objective optimization intelligent algorithms have been rapidly developed, and various algorithms with excellent performance indicators have emerged, such as DNEA, HREA, SMPSO, etc. Compared with these, the second-generation NSGA2 (non-dominated sorting genetic algorithm II) with an elite retention strategy does not have outstanding performance in fast non-dominated sorting algorithms [24]. However, as a classic multi-objective optimization algorithm, the NSGA2 algorithm has been successfully applied in multiple fields, proving its applicability and practicality.…”

Section: Response Surface Methods and Nsga2 Multi-objective Optimizat...mentioning

confidence: 99%

Multi-Objective Optimization of a Multi-Cavity, Significant Wall Thickness Difference Extrusion Profile Mold Design for New Energy Vehicles

Xu,

Jiang,

et al. 2024

Materials

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With the rapid development of the new energy vehicle market, the demand for extruded profiles for battery trays, mainly characterized by significant wall thickness differences in multiple chambers, is increasing, posing new challenges to production and quality control. This study examines the multi-objective optimization problem in the design process of aluminum profile dies with multi-cavity profiles and significant wall thickness differences. Using QFORM-extrusion professional aluminum extrusion finite element analysis software and the response surface analysis method, the standard deviation of the velocity (SDV), standard deviation of the pressure (SDP), and thick wall hydrostatic pressure (TWHP) on the profile section at the die exit are optimized. By analyzing the functional relationship between the key die structure parameters (the height of the baffle plates, the length of the bearing, and the height of the false mandrel) and the optimization objective, the optimal combination scheme of die structure parameters was obtained using the NSGA2 (non-dominated sorting genetic algorithm-2) multi-objective genetic optimization algorithm. The results show that, compared with the initial design scheme, the standard deviation of profile section velocity was reduced by 5.33%, the standard deviation of pressure was reduced by 11.16%, and the thick wall hydrostatic pressure was increased by 26.47%. The die designed and manufactured using this scheme successfully completed the hot extrusion production task, and the profile quality met the predetermined requirements, thus verifying the effectiveness of this study in optimizing the design of a multi-cavity aluminum profile die with significant differences in wall thickness for complex structures.

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Section: Response Surface Methods and Nsga2 Multi-objective Optimizat...mentioning

confidence: 99%

Multi-Objective Optimization of a Multi-Cavity, Significant Wall Thickness Difference Extrusion Profile Mold Design for New Energy Vehicles

Xu,

Jiang,

et al. 2024

Materials

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“…Decomposition-Based Evolutionary Algorithm (DBEA) The decomposition has been the mainstream approach in classic mathematical programming for multi-objective optimization and multi-criterion decision-making. DBEA's algorithm [Li 2021] is based on this mathematical model of decomposition. That is, the population size µ is dynamically changed during the search process.…”

Section: Search Enginesmentioning

confidence: 99%

Assessing Multi-Objective Search Engines for GE: A Case Study in CNN Generation

Chagas,

Rosa,

Silva

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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In recent years, the number of available Convolutional Neural Networks (CNNs) has increased significantly, making it difficult to select an appropriate CNN for a specific problem. To address this challenge, researchers have proposed automated techniques for optimizing CNN architectures, with Grammatical Evolution (GE) being one of the most promising approaches. GE uses context-free grammar to generate programs (e.g., CNNs) and a search engine to find the best solutions. Although several grammars have been proposed for CNN generation, there has been no research evaluating the impact of different search engines in the GE optimization process. This study treats the CNN generation as a multi-objective problem by optimizing accuracy and F1-score, and evaluates seven different multi-objective optimizers listed in the literature as potential search engines. The goal is to investigate the strengths and weaknesses of each optimizer in CNN generation. The experiments were performed on the widely-used CIFAR-10 image classification dataset, and the results showed that selecting the right optimizer for the task is crucial and can have a significant impact on the final result, especially when the number of generations is limited.

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“…The multi-objective evolutionary algorithm performs well when faced with these problems. Li Wenhua first reviewed the related works about multimodal multi-objective problems (MMOPs) in the literature [17], then chose 15 state-of-the-art algorithms that utilize different diversity-maintaining techniques and compared their performance on different types of the existing test suites. Later, Li Wenhua proposed an evolutionary algorithm with a hierarchy ranking method (HREA) to find both the global and the local PFs based on the decision-maker's preference [18], then presented a novel multimodal multi-objective coevolutionary algorithm (CoMMEA) to better produce both global and local PSs, and simultaneously to improve the convergence performance in dealing with high-dimension MMOPs [19].…”

Section: Introductionmentioning

confidence: 99%

Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA

Li,

Zhang,

2024

Applied Sciences

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This paper presents a two-step approach for optimizing the configuration of a mobile photovoltaic-diesel-storage microgrid system. Initially, we developed a planning configuration model to ensure a balance between the mobility of components and a sustainable power supply. Then, we introduced a method that merges optimization and decision-making. The first phase identifies Pareto optimal solutions (POSs) with a favorable distribution by using a multi-objective evolutionary algorithm with classification-based preselection (CPS-MOEA). In the second phase, we utilize the fuzzy C-means algorithm (FCM) and the grey relational projection (GRP) method for comprehensive decision-making. This aims to select the most suitable and compromise solution from the POSs, closely aligning with the decision-maker’s preferences. Beyond addressing the optimal planning and configuration issue, the experimental results show that the method surpasses other widely used multi-objective optimization algorithms, including the Preference Inspired Co-evolution Algorithm (PICEA-g), the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO), and the third stage of Generalized Differential Evolution (GDE3).

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Multimodal multi-objective optimization: Comparative study of the state-of-the-art

Cited by 49 publications

References 36 publications

Multi-Objective Optimization of a Multi-Cavity, Significant Wall Thickness Difference Extrusion Profile Mold Design for New Energy Vehicles

Multi-Objective Optimization of a Multi-Cavity, Significant Wall Thickness Difference Extrusion Profile Mold Design for New Energy Vehicles

Assessing Multi-Objective Search Engines for GE: A Case Study in CNN Generation

Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA

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