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Article
Computer Science, Artificial Intelligence
Yutao Yang et al.
Summary: The research proposes a population-based optimization technique called Hunger Games Search (HGS), designed based on the hunger-driven activities and behavioral choices of animals, with a simple structure, special stability features, and competitive performance to efficiently address constrained and unconstrained problems.
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(2021)
Article
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Shengchao Zhou et al.
Summary: This article discusses a single BPM scheduling problem with unequal release times and job sizes, proposing a self-adaptive differential evolution algorithm to address the issue. Experimental results show that the proposed algorithm is more effective in solving the scheduling problem compared to other existing algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Fuqing Zhao et al.
Summary: This article proposes a two-stage cooperative evolutionary algorithm called TS-CEA to address the energy-efficient scheduling of the no-wait flow-shop problem (EENWFSP). By optimizing both makespan and total energy consumption criteria, TS-CEA demonstrates effectiveness and efficiency in improving solution quality.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Iman Ahmadianfar et al.
Summary: The optimization field is plagued by metaphor-based pseudo-novel or fancy optimizers, with limited contributions to the optimization process. This study introduces a novel metaphor-free population-based optimization method called RUNge Kutta optimizer (RUN) based on mathematical foundations, showing promising results in mathematical tests and engineering problems. The RUN algorithm utilizes slope variations computed by the RK method for global optimization, demonstrating superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance.
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(2021)
Article
Engineering, Multidisciplinary
Chengtian Ouyang et al.
Summary: This paper introduces a lens learning sparrow search algorithm (LLSSA) to improve the deficiencies of the new sparrow search algorithm. By incorporating lens learning and spiral search strategy, the algorithm enhances search and exploration capabilities, showing good performance in function optimization and three-dimensional UAV path planning.
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Article
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Qiankun Liang et al.
Summary: In this paper, a new intelligent optimization algorithm called Sparrow Search Algorithm (SSA) and its modification are applied for the first time in the electromagnetics and antenna community to solve the antenna array optimization problem, showing advantages in convergence accuracy, convergence speed, and stability. Experimental results demonstrate the effectiveness of the modified algorithm in optimizing the element positions and excitation amplitudes of linear antenna arrays, as well as reducing the maximum side lobe level (SLL).
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
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Article
Mathematical & Computational Biology
Chengtian Ouyang et al.
Summary: This paper proposes an improved learning sparrow search algorithm, which enhances the algorithm's performance and robustness in optimization problems by introducing lens reverse learning strategy and differential-based local search strategy.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2021)
Article
Engineering, Multidisciplinary
Jiaze Tu et al.
Summary: This paper introduces a new stochastic optimizer called the Colony Predation Algorithm (CPA) based on the predation behavior of animals in nature, utilizing mathematical mapping to improve algorithm performance by simulating strategies used by animal hunting groups. The proposed CPA demonstrates competitive performance in optimizing engineering problems and will provide publicly available source code after publication.
JOURNAL OF BIONIC ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Radu-Emil Precup et al.
Summary: This paper introduces five new contributions to the state-of-the-art in fuzzy controller tuning. It proposes a fresh metaheuristic algorithm, the Slime Mould Algorithm (SMA), for optimal tuning of cost-effective fuzzy controllers. The study also presents a real-world application of SMA focusing on the optimal tuning of TSK PI-FCs for nonlinear servo systems, highlighting the superiority of SMA over other metaheuristic algorithms in solving the same optimization problem.
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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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(2018)
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Seyedali Mirjalili et al.
ADVANCES IN ENGINEERING SOFTWARE
(2016)
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Seyedali Mirjalili
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Seyedali Mirjalili et al.
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