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An improved farmland fertility algorithm for many-objective optimization problems [PDF]
Scientific Reports, 2022 Recent studies on many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. However, difficulties in scalability upon MaOPs including the selection of leaders,
Yanjiao Wang, Peng Gao, Ye Chen
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Many-objective African vulture optimization algorithm: A novel approach for many-objective problems.
PLoS ONE, 2023 Several optimization problems can be abstracted into many-objective optimization problems (MaOPs). The key to solving MaOPs is designing an effective algorithm to balance the exploration and exploitation issues. This paper proposes a novel many-objective
Heba Askr +4 more
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A many-objective Jaya algorithm for many-objective optimization problems [PDF]
Decision Science Letters, 2018 The proposed work presents the design and application of many-objective Jaya (MaOJaya) algorithm to optimize many-objective benchmark optimization problems.
Sandeep U. Mane +2 more
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International Journal of Computational Intelligence SystemsIn this paper, a novel Many-Objective Whale Optimization Algorithm (MaOWOA) is proposed to overcome the challenges of large-scale many-objective optimization problems (LSMOPs) encountered in diverse fields such as engineering.
Kanak Kalita +6 more
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Reference Point and Grid Method-Based Evolutionary Algorithm with Entropy for Many-Objective Optimization Problems [PDF]
EntropyIn everyday scenarios, there are many challenges involving multi-objective optimization. As the count of objective functions rises to four or beyond, the problem’s complexity intensifies considerably, often making it challenging for traditional ...
Qi Leng, Bo Shan, Chong Zhou
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TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems. [PDF]
PLoS ONELarge-scale many-objective optimization problems (LSMaOPs) are a current research hotspot. However, since LSMaOPs involves a large number of variables and objectives, state-of-the-art methods face a huge search space, which is difficult to be explored ...
Xiaozhi Du +3 more
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International Journal of Computational Intelligence SystemsIn metaheuristic multi-objective optimization, the term effectiveness is used to describe the performance of a metaheuristic algorithm in achieving two main goals—converging its solutions towards the Pareto front and ensuring these solutions are well ...
Kanak Kalita +4 more
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An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems [PDF]
Algorithms, 2017 MOEA/D is a promising multi-objective evolutionary algorithm based on decomposition, and it has been used to solve many multi-objective optimization problems very well.
Wei Zheng +3 more
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Handling Constrained Many-Objective Optimization Problems via Problem Transformation [PDF]
IEEE Transactions on Cybernetics, 2021 Objectives optimization and constraints satisfaction are two equally important goals to solve constrained many-objective optimization problems (CMaOPs). However, most existing studies for CMaOPs can be classified as feasibility-driven-constrained many-objective evolutionary algorithms (CMaOEAs), and they always give priority to satisfy constraints ...
Ruwang Jiao +4 more
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Scalable and customizable benchmark problems for many-objective optimization [PDF]
Applied Soft Computing, 2020 Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective optimization (MOO) field. One way to measure algorithm performance is through the use of benchmark functions (also called test functions or test suites), which are artificial problems with a well-defined mathematical formulation, known solutions and a ...
Ivan Reinaldo Meneghini +3 more
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