Poplar optimization algorithm: A new meta-heuristic optimization technique for numerical optimization and image segmentation

Chen, Debao; Ge, Yuanyuan; Wan, Yujie; Deng, Yu; Chen, Yuan; Zou, Feng

doi:10.1016/j.eswa.2022.117118

Cited by 36 publications

(6 citation statements)

References 22 publications

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“…( 2020 ) 373 Poplar Optimization Algorithm (POA) Chen et al. ( 2022 ) 374 POPMUSIC Taillard and Voss ( 2002 ) 375 Population Migration Algorithm (PMA) ZongXXSlahUndXXyuan ( 2003 ) 376 Prairie Dog Optimization (PDO) Ezugwu et al. ( 2022 ) 377 Predator–Prey Optimization (PPO) Narang et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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“…This integration of information reduces the risk of getting trapped in local optima [19]. Additionally, with multiple solutions exploring a wide range of the search space, population-based methods are more likely to yield superior final results compared to single solution-based methods [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…In Equation (23), n represents the number of non-dominated solutions found by the algorithm; d i represents the Euclidean distance between the i-th solution in the nondominated set found by the algorithm and its nearest neighbor solution; d represents the average of all d i .…”

mentioning

confidence: 99%

An Advanced Multi-Objective Ant Lion Algorithm for Reservoir Flood Control Optimal Operation

Ning,

Ren,

Guo

et al. 2024

Water

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Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

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“… 2021 Ebola Optimization Search Algorithm [ 27 ] Propagation mechanism of the Ebola virus Image classification of digital mammography 2021 Rock Hyraxes Swarm Optimization [ 28 ] Collective behavior of Rock Hyraxes Benchmark functions 2021 Red Colobuses Monkey [ 29 ] Behavior related to red monkeys Benchmark functions 2021 African vultures optimization algorithm [ 30 ] African vultures' lifestyle Engineering problems 2021 Battle royale optimization algorithm [ 31 ] Digital games knowns as “battle royale.” The inverse kinematics problem of the 6-DOF PUMA 560 robot arm 2021 Artificial Jellyfish Search [ 32 ] Jellyfish behavior Benchmark functions and optimization problems. 2021 Poplar Optimization Algorithm [ 33 ] Sexual and asexual propagation mechanism of poplar Image segmentation 2022 Snake Optimizer [ 34 ] Special mating behavior of snakes Constrained real-world engineering problems 2022 War Strategy Optimization [ 35 ] Strategic movement of army troops during the war Engineering models 2022 Honey Badger algorithm [ 36 ] Intelligent foraging behavior of honey badger Engineering design pr...…”

Section: Introductionmentioning

confidence: 99%

Greater cane rat algorithm (GCRA): A nature-inspired metaheuristic for optimization problems

Agushaka,

Ezugwu,

Saha

et al. 2024

Heliyon

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Poplar optimization algorithm: A new meta-heuristic optimization technique for numerical optimization and image segmentation

Cited by 36 publications

References 22 publications

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

An Advanced Multi-Objective Ant Lion Algorithm for Reservoir Flood Control Optimal Operation

Greater cane rat algorithm (GCRA): A nature-inspired metaheuristic for optimization problems

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