Article
Computer Science, Artificial Intelligence
Shuo-Peng Gong, Mohammad Khishe, Mokhtar Mohammadi
Summary: This paper addresses two important concerns in handling multimodal problems and proposes the Chimp Optimization Algorithm (NChOA) with embedded niching technique. Through comparisons and experiments, the NChOA demonstrates excellent performance in multiple numerical functions and engineering problems, making it more applicable in a wide range of engineering applications.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Fernando G. Lobo, Mosab Bazargani
Summary: This article investigates the performance of various optimization algorithms on instances of a multimodal problem generator, presenting a runtime analysis for this class of problems and revealing that traditional niching and mating restriction techniques are not sufficient to make evolutionary algorithms competitive with hillclimbing strategies.
EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Wenhao Du, Zhigang Ren, An Chen, Hanqing Liu, Yichuan Wang, Haoxi Leng
Summary: In this study, a multi-niche cooperation based MMEA algorithm is proposed to address the issue of independent handling of multiple populations in existing MMEAs. The algorithm includes a knowledge transfer strategy (KTS) and a collaborative search mechanism (CSM). Experiments demonstrate that the combination of KTS and CSM gives MNC-NEA a significant competitive advantage in solving multimodal optimization problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Cuicui Yang, Tongxuan Wu, Junzhong Ji
Summary: This study proposes a multimodal multi-objective optimization evolutionary algorithm based on two-stage species conservation to solve MMOPs with local PSs. The algorithm divides the evolutionary process into diversity-oriented species conservation and convergence-oriented species conservation. Experimental results demonstrate the algorithm's ability to find global and local PSs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Qingxue Liu, Shengzhi Du, Barend Jacobus van Wyk, Yanxia Sun
Summary: Multimodal optimization aims to find and maintain global and local optima of a function. Niching techniques based on multi-populations and clustering have been proven efficient. The proposed DLCSDE algorithm and its improved version SDLCSDE show superior performance compared to 17 state-of-art niching algorithms on various multi modal problems.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Ali Ahrari, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: This study proposes a new method to generate deterministic DMMO test problems that can simulate a wider range of challenges. By controlling the intensity of each challenge, users can pinpoint the pros and cons of DMMO methods accurately.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Kai Wang, Wenyin Gong, Libao Deng, Ling Wang
Summary: This paper proposes a dynamically hybrid niching-based differential evolution algorithm (DHNDE) for solving multimodal optimization problems (MMOPs). The DHNDE algorithm achieves a good tradeoff between diversity and convergence by dynamically using two niching techniques, introducing a secondary archive, and improving the neighborhood speciation-based DE. Experimental results demonstrate that DHNDE provides highly competitive results compared to other methods, especially for MMOPs with a large number of global optima.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Boyang Qu, Guosen Li, Li Yan, Jing Liang, Caitong Yue, Kunjie Yu, Oscar D. Crisalle
Summary: This paper proposes a grid-guided particle swarm optimizer for solving multimodal multi-objective optimization problems. By using a grid in the decision space, the algorithm is able to detect promising subregions and generate multiple subpopulations, maintaining diversity and improving search efficiency. Experimental results demonstrate that the proposed algorithm outperforms other evolutionary methods.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Xiao-Min Hu, Shou-Rong Zhang, Min Li, Jeremiah D. Deng
Summary: The purpose of feature selection is to eliminate redundant and irrelevant features and leave useful features for classification. Existing algorithms mainly focus on finding one best feature subset, neglecting the fact that the problem may have more than one best feature subset. A novel multimodal niching particle swarm optimization algorithm is proposed to find out all the best feature combinations in a feature selection problem.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Ali Ahrari, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: This study introduces a second variant of the successful RS-CMSA-ES method, called RS-CMSA-ESII, which improves upon certain components and enhances the performance of the method in multimodal optimization.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Chemistry, Multidisciplinary
Rasel Ahmed, Amril Nazir, Shuhaimi Mahadzir, Mohammad Shorfuzzaman, Jahedul Islam
Summary: This study proposes a niching Grey Wolf Optimizer (NGWO) that combines personal best features of PSO and a local search technique to address issues in multi-modal optimization. Tested on benchmark functions and engineering cases, the algorithm outperformed all other considered algorithms, indicating its effectiveness in solving optimization problems.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Jialiang Sun, Xianqi Chen, Jun Zhang, Wen Yao
Summary: This paper explores a multimodal optimization method for satellite layout optimization design and proposes an improved niching-based cross-entropy method. Through investigations on CEC2013 benchmarks and satellite layout optimization design problem, the effectiveness and feasibility of the proposed method are validated, showing superior performance compared to several state-of-the-art algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Junbo Liu, Ji Zhou, Dajie Yu, Haifeng Sun, Song Hu, Jian Wang
Summary: In this study, a source optimization method based on a hybrid genetic algorithm is proposed to achieve an acceptable source shape in the imaging process for optical lithography. The method utilizes particle swarm optimization and the tabu list method from the tabu search algorithm to overcome the problems of local optima and small search scope, enhancing the optimization performance. Different feature patterns are employed as the input of the optimization model. Simulation results show that the proposed method exhibits dominant optimization performance for source optimization in optical lithography.
APPLIED SCIENCES-BASEL
(2023)
Article
Metallurgy & Metallurgical Engineering
Xu Zhe, Ni Wei-chen, Ji Yue-hui
Summary: Randomness is crucial in ensemble learning, and a common practice is to rotate feature space randomly. However, this requires a large number of trees and computing resources. The MGARF algorithm proposed in this paper utilizes multimodal genetic algorithm to select diverse and accurate base learners, outperforming random forest and random rotation methods on classification datasets.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2021)
Article
Mathematics
Ziwei Lin, Andrea Matta, Sichang Du, Evren Sahin
Summary: A multimodal optimization task aims to find multiple global optima and high-quality local optima of an optimization problem. In this paper, a partition-based random search method is proposed to iteratively partition the feasible domain and exploit promising regions earlier. The method demonstrates good performance in benchmark functions with multiple global optima.
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
