Exploratory cuckoo search for solving single-objective optimization problems

Abed-alguni, Bilal H.; Alawad, Noor Aldeen; Barhoush, Malek; Hammad, Rafat

doi:10.1007/s00500-021-05939-3

Cited by 49 publications

(16 citation statements)

References 44 publications

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“…ROL strategy combining the refraction principle [ 28 , 34 ] from physics with an OBL strategy is a strong method to strengthen MAs [ 28 , 29 , 34 , 35 ]. In this paper, the ROL strategy is applied to augment the performance of the BROMLDE algorithm.…”

Section: The Proposed Bromlde Algorithmmentioning

confidence: 99%

“…To be specific, in [ 28 ], a refracted oppositional learning (ROL) strategy is incorporated into the artificial bee colony algorithm, promoting the diversity of the population and guiding it to explore the global optimal solution. Based on the ROL strategy, the authors in [ 29 ] propose a cuckoo search algorithm with refraction learning, improving the capability of cuckoo search to avoid local optimal positions. Regrettably, their suitable scale factors are difficult to select.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

Zhang

et al. 2022

Entropy

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Numerical optimization has been a popular research topic within various engineering applications, where differential evolution (DE) is one of the most extensively applied methods. However, it is difficult to choose appropriate control parameters and to avoid falling into local optimum and poor convergence when handling complex numerical optimization problems. To handle these problems, an improved DE (BROMLDE) with the Bernstein operator and refracted oppositional-mutual learning (ROML) is proposed, which can reduce parameter selection, converge faster, and avoid trapping in local optimum. Firstly, a new ROML strategy integrates mutual learning (ML) and refractive oppositional learning (ROL), achieving stochastic switching between ROL and ML during the population initialization and generation jumping period to balance exploration and exploitation. Meanwhile, a dynamic adjustment factor is constructed to improve the ability of the algorithm to jump out of the local optimum. Secondly, a Bernstein operator, which has no parameters setting and intrinsic parameters tuning phase, is introduced to improve convergence performance. Finally, the performance of BROMLDE is evaluated by 10 bound-constrained benchmark functions from CEC 2019 and CEC 2020, respectively. Two engineering optimization problems are utilized simultaneously. The comparative experimental results show that BROMLDE has higher global optimization capability and convergence speed on most functions and engineering problems.

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Section: The Proposed Bromlde Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

Zhang

et al. 2022

Entropy

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“…Cuckoo search (CS) is a mathematical optimization algorithm inspired by the nesting and parasitic reproduction behaviors of some cuckoo species and the Lévy flight behavior of some fruit flies and birds [25,26]. It has been successfully applied to solve various optimization problems because of its few parameters and easy implementation [27,28]. The CS algorithm uses Lévy flight to generate a new solution.…”

Section: Introductionmentioning

confidence: 99%

Design and Parameter Optimization of Trajectory Correction Control Strategy for Air Duct Structure Projectile

Cui

Liu

Jiang

et al. 2023

International Journal of Aerospace Engineering

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In order to make the new air duct structure trajectory correction projectile have good dynamic correction control effect, the control strategy of the projectile’s correction mechanism is studied in this paper. A design method of trajectory correction control strategy based on particle swarm optimization-cuckoo search (PSO-CS) hybrid algorithm is proposed to obtain the optimal control parameters that can make the projectile flight stable and correct accurately. Firstly, the mathematical model of the air duct structure projectile is established. Secondly, the multiobjective optimization problem is analyzed. The projectile’s correction control strategy optimization model is established by taking the start control time, the number of corrections, the correction working time, and the interval time as the control variables. The optimization model innovatively considers the influence of the correction action on the flight stability of the projectile and the influence of the start control time on the correction range. Finally, the PSO-CS hybrid algorithm is used to design the calculation method of the optimization model and solve the optimal correction working parameters. The simulation results indicate that the control strategy optimization model can be solved by the proposed calculation method. Moreover, optimal correction working parameters of the correction mechanism in the current state can be obtained. Compared with the results of using single PSO algorithm and CS algorithm, the correction scheme calculated by PSO-CS hybrid optimization algorithm is better. This correction control scheme can effectively reduce the impact point deviation and make the projectile flight stable. At the same time, the circular error probable (CEP) of the projectile after correction is reduced from 42.3 m to 4.6 m while the impact point dispersion is lowered. The research results show that the design method of correction control strategy proposed in this paper is effective for trajectory correction of the new air duct structure projectile.

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“…Metaheuristics are general frameworks to build heuristics for combinatorial and global optimization problems [ 3 ]. The application of natural or biology-inspired metaheuristic optimizations, such as Genetic Algorithm [ 4 ], Particle Swarm Optimization [ 5 ], Harmony Search [ 6 ], Differential Evolution (DE) [ 7 – 10 ], Artificial Bee Colony [ 11 ], Fruit Fly Optimization [ 12 ], Distributed Grey Wolf Optimizer (DGWO) [ 13 ], Moth Search Algorithm (MSA) [ 14 ], Slime Mould Algorithm (SMA) [ 15 ], Gaining Sharing Knowledge-Based Optimization [ 16 , 17 ], Cuckoo Search with Exploratory (ECS) [ 18 ], Discrete Jaya with Refraction Learning and Three Mutation (DJRL3M) [ 19 ], and Monarch Butterfly Optimization (MBO) [ 20 ], Hunger Games Search (HGS) [ 21 ], Runge Kutta Method (RUN) [ 22 ], and Harris Hawks Optimization (HHO) [ 23 ], has been very successful to solve the complex optimization problems, such as feature selection [ 24 – 28 ], image segmentation [ 29 ], controller designation [ 30 ], flow-shop scheduling problem [ 31 , 32 ], and the node placement of wireless sensor networks [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Differential Human Learning Optimization Algorithm

Zhang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.

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Exploratory cuckoo search for solving single-objective optimization problems

Cited by 49 publications

References 44 publications

An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

Design and Parameter Optimization of Trajectory Correction Control Strategy for Air Duct Structure Projectile

Differential Human Learning Optimization Algorithm

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