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Yildizdan G, Baş E. A Novel Binary Artificial Jellyfish Search Algorithm for Solving 0–1 Knapsack Problems. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11171-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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2
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An enhanced multi-operator differential evolution algorithm for tackling knapsack optimization problem. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08358-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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3
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Binary African vultures optimization algorithm for various optimization problems. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01703-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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4
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Yan Y, Xia X, Zhang L, Li Z, Qin C. A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1366. [PMID: 37420386 DOI: 10.3390/e24101366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 07/09/2023]
Abstract
With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.
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Affiliation(s)
- Yonghang Yan
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
- Henan Province Engineering Research Center of Spatial Information Processing, Henan University, Kaifeng 475004, China
| | - Xuewen Xia
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
| | - Lingli Zhang
- Beijing Aerospace Automatic Control Institute, Beijing 100854, China
| | - Zhijia Li
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
| | - Chunbin Qin
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China
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Zhang Z, Gao Y. Solving large-scale global optimization problems and engineering design problems using a novel biogeography-based optimization with Lévy and Brownian movements. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01642-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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6
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Harifi S. A binary ancient-inspired Giza Pyramids Construction metaheuristic algorithm for solving 0-1 knapsack problem. Soft comput 2022. [DOI: 10.1007/s00500-022-07285-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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7
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Modified multiverse optimizer technique-based two degree of freedom fuzzy PID controller for frequency control of microgrid systems with hydrogen aqua electrolyzer fuel cell unit. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07453-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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8
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An improved generalized normal distribution optimization and its applications in numerical problems and engineering design problems. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10182-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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9
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Modified Whale Optimization Algorithm for Multi-Type Combine Harvesters Scheduling. MACHINES 2022. [DOI: 10.3390/machines10010064] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The optimal scheduling of multi-type combine harvesters is a crucial topic in improving the operating efficiency of combine harvesters. Due to the NP-hard property of this problem, developing appropriate optimization approaches is an intractable task. The multi-type combine harvesters scheduling problem considered in this paper deals with the question of how a given set of harvesting tasks should be assigned to each combine harvester, such that the total cost is comprehensively minimized. In this paper, a novel multi-type combine harvesters scheduling problem is first formulated as a constrained optimization problem. Then, a whale optimization algorithm (WOA) including an opposition-based learning search operator, adaptive convergence factor and heuristic mutation, namely, MWOA, is proposed and evaluated based on benchmark functions and comprehensive computational studies. Finally, the proposed intelligent approach is used to solve the multi-type combine harvesters scheduling problem. The experimental results prove the superiority of the MWOA in terms of solution quality and convergence speed both in the benchmark test and for solving the complex multi-type combine harvester scheduling problem.
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A novel hybrid firefly–whale optimization algorithm and its application to optimization of MPC parameters. Soft comput 2021. [DOI: 10.1007/s00500-021-06441-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Abuhamdah A. Adaptive elitist-ant system for solving combinatorial optimization problems. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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13
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A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems. PROGRESS IN ARTIFICIAL INTELLIGENCE 2021. [DOI: 10.1007/s13748-021-00244-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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14
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Optimal Scheduling of Non-Convex Cogeneration Units Using Exponentially Varying Whale Optimization Algorithm. ENERGIES 2021. [DOI: 10.3390/en14041008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper proposes an Exponentially Varying Whale Optimization Algorithm (EVWOA) to solve the single-objective non-convex Cogeneration Units problem. This problem seeks to evaluate the optimal output of the generator unit to minimize a CHP system’s fuel costs. The nonlinear and non-convex characteristics of the objective function demands a powerful optimization technique. The traditional Whale Optimization Algorithm (WOA) is improved by incorporating four different acceleration functions to fine-tune its performance during exploration and exploitation phases. Among the four variants of the proposed WOA, the emphasis is laid on the EVWOA which uses the exponentially varying acceleration function (EVAF). The proposed EVWOA is tested on six different small-scale to large-scale systems. The results obtained for these six test systems, followed by a statistical study highlight the supremacy of EVWOA for finding the best optimal solution and the convergence traits.
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15
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A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106903] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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16
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Li Y, He Y, Liu X, Guo X, Li Z. A novel discrete whale optimization algorithm for solving knapsack problems. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01722-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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An extensive review of computational intelligence-based optimization algorithms: trends and applications. Soft comput 2020. [DOI: 10.1007/s00500-020-04958-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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18
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A direct method for solving calculus of variations problems using the whale optimization algorithm. EVOLUTIONARY INTELLIGENCE 2019. [DOI: 10.1007/s12065-019-00275-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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19
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Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm. MATHEMATICS 2019. [DOI: 10.3390/math7080688] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The flexible job shop scheduling problem (FJSP) is a difficult discrete combinatorial optimization problem, which has been widely studied due to its theoretical and practical significance. However, previous researchers mostly emphasized on the production efficiency criteria such as completion time, workload, flow time, etc. Recently, with considerations of sustainable development, low-carbon scheduling problems have received more and more attention. In this paper, a low-carbon FJSP model is proposed to minimize the sum of completion time cost and energy consumption cost in the workshop. A new bio-inspired metaheuristic algorithm called discrete whale optimization algorithm (DWOA) is developed to solve the problem efficiently. In the proposed DWOA, an innovative encoding mechanism is employed to represent two sub-problems: Machine assignment and job sequencing. Then, a hybrid variable neighborhood search method is adapted to generate a high quality and diverse population. According to the discrete characteristics of the problem, the modified updating approaches based on the crossover operator are applied to replace the original updating method in the exploration and exploitation phase. Simultaneously, in order to balance the ability of exploration and exploitation in the process of evolution, six adjustment curves of a are used to adjust the transition between exploration and exploitation of the algorithm. Finally, some well-known benchmark instances are tested to verify the effectiveness of the proposed algorithms for the low-carbon FJSP.
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Khadanga RK, Kumar A, Panda S. A novel modified whale optimization algorithm for load frequency controller design of a two-area power system composing of PV grid and thermal generator. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04321-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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21
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A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:8718571. [PMID: 31231431 PMCID: PMC6512044 DOI: 10.1155/2019/8718571] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 03/13/2019] [Accepted: 03/18/2019] [Indexed: 11/30/2022]
Abstract
The whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC and PSO. Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta-analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey's results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.
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23
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Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm. MATHEMATICS 2018. [DOI: 10.3390/math6110220] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO2 emission, are seldom considered in the traditional production scheduling problems. Recently, the energy-related scheduling problem has been paid increasingly more attention by researchers. In this paper, an energy-efficient job shop scheduling problem (EJSP) is investigated with the objective of minimizing the sum of the energy consumption cost and the completion-time cost. As the classical JSP is well known as a non-deterministic polynomial-time hard (NP-hard) problem, an improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem. The improvement is performed using dispatching rules (DR), a nonlinear convergence factor (NCF), and a mutation operation (MO). The DR is used to enhance the initial solution quality and overcome the drawbacks of the random population. The NCF is adopted to balance the abilities of exploration and exploitation of the algorithm. The MO is employed to reduce the possibility of falling into local optimum to avoid the premature convergence. To validate the effectiveness of the proposed algorithm, extensive simulations have been performed in the experiment section. The computational data demonstrate the promising advantages of the proposed IWOA for the energy-efficient job shop scheduling problem.
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