Thewaterwave optimization (WWO) algorithm inspired by shallowwaterwave theory, which has the disadvantages of falling easily into local optimal solution and slower convergence speed and lower calculation accuracy. Con...
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Thewaterwave optimization (WWO) algorithm inspired by shallowwaterwave theory, which has the disadvantages of falling easily into local optimal solution and slower convergence speed and lower calculation accuracy. Concerning this issue, an improved sine cosineWWO algorithm (SCWWO) used elite opposition-based is proposed and to solve optimizationfunctions and structure engineering design problems. First of all, WWO algorithm is combined with the sine cosine algorithm (SCA) in parallel to wave propagation and breaking, because water wave waveform and sine and cosine curves are extremely similar and the SCA algorithm has strong global search capability to improve WWO algorithm's exploitation and exploration capabilities. Secondly, the elite opposition-based learning strategy is introduced into the wave refraction operation that increases the diversity of the population and enhances the exploration capability of WWO algorithm. The SCWWO algorithm clearly improves convergence speed and calculation accuracy. The SCWWO algorithm is compared using 9 benchmark functions. The experimental results demonstrate the feasibility and efficiency of the proposed SCWWO algorithm.
Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g...
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Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Levy flight (SSACL) is proposed. The crossover scheme and Levy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon's rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.
The slime mould algorithm (SMA) is a metaheuristic algorithm recently proposed, which is inspired by the oscillations of slime mould. Similar to other algorithms, SMA also has some disadvantages such as insufficient b...
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The slime mould algorithm (SMA) is a metaheuristic algorithm recently proposed, which is inspired by the oscillations of slime mould. Similar to other algorithms, SMA also has some disadvantages such as insufficient balance between exploration and exploitation, and easy to fall into local optimum. This paper, an improved SMA based on dominant swarm with adaptive t-distribution mutation (DTSMA) is proposed. In DTSMA, the dominant swarm is used improved the SMA's convergence speed, and the adaptive t-distribution mutation balances is used enhanced the exploration and exploitation ability. In addition, a new exploitation mechanism is hybridized to increase the diversity of populations. The performances of DTSMA are verified on CEC2019 functions and eight engineering design problems. The results show that for the CEC2019 functions, the DTSMA performances are best;for the engineering problems, DTSMA obtains better results than SMA and many algorithms in the literature when the constraints are satisfied. Furthermore, DTSMA is used to solve the inverse kinematics problem for a 7-DOF robot manipulator. The overall results show that DTSMA has a strong optimization ability. Therefore, the DTSMA is a promising metaheuristic optimization for global optimization problems.
Grasshopper optimization algorithm (GOA) is proposed for imitating grasshopper's behavior in nature, which has the disadvantages of slow convergence speed and unbalanced exploration and exploitation, etc. Therefor...
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Grasshopper optimization algorithm (GOA) is proposed for imitating grasshopper's behavior in nature, which has the disadvantages of slow convergence speed and unbalanced exploration and exploitation, etc. Therefore, an algorithm called GOA_jDE, which combines GOA and jDE is proposed to improve the optimization performance. Firstly, the adaptive strategy is introduced into DE to improve the global search ability in the proposed algorithm. Secondly, the combination of jDE and GOA greatly improves the convergence efficiency while maintaining the population diversity. Finally, it can be observed in the work that the proposed algorithm improves the convergence speed and calculation precision. In the subsequent experiments, 14 well-known test benchmark functions are used to compare the advantages of GOA_jDE. The experimental results illustrate that the performance of proposed algorithm has significant improvement, which also proves the feasibility and effectiveness. Considering the complexity of engineering problems, three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) are used to evaluate the performance of the proposed algorithm. In addition, the classical engineering design results proves the merits of this algorithm in solving real problems with unknown search spaces.
作者：
Liu, JingsenLiu, XiaozhenLi, YuHenan Univ
Coll Software Inst Intelligent Network Syst Kaifeng 475004 Peoples R China Henan Univ
Coll Software Kaifeng 475004 Peoples R China Henan Univ
Inst Management Sci & Engn Kaifeng 475004 Peoples R China
In order to better apply the cuckoo search (CS) algorithm in solving the problem of function extremism optimization, and further improve the phenomenon of low precision and slow convergence in the optimization process...
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In order to better apply the cuckoo search (CS) algorithm in solving the problem of function extremism optimization, and further improve the phenomenon of low precision and slow convergence in the optimization process of algorithm, the two subpopulations CS algorithm based on mean value evaluation is proposed. On the one hand, the algorithm introduces dynamic inertia weight to adjust the levy flight mechanism, thus dynamically constraining the moving step-size of each generation of population, so that the algorithm has certain self-adaptability. On the other hand, the algorithm changes the way of mutation in the preference random walk. First, the average fitness evaluation mechanism is used to divide the current population into two subpopulations: good and bad. Then, it adopts a directional mutation strategy for the better population, so that the individual can search purposefully. The worse population uses differential mutation mechanism of the disturbance items with the t-distribution characteristics, and makes the individual to search in the best orientation of current, so as to enhance the local search performance and accelerate the convergence rate of the algorithm. Theoretical analysis proves the convergence and time complexity of the algorithm in this paper. The simulation results show that the improved algorithm has good applicability in solving the function optimization problem, and the optimization results and convergence speed have been significantly improved in the algorithm.
The on-line performance and off-line performance are provided to evaluate the performances of Election Campaign optimization (ECO) algorithm used in functions optimization. The relatively between the selected paramete...
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ISBN:
(纸本)9781457715846
The on-line performance and off-line performance are provided to evaluate the performances of Election Campaign optimization (ECO) algorithm used in functions optimization. The relatively between the selected parameters of ECO and the algorithm performance is analyzed by researching the numerical optimal problem of standard test functions. It is expected to observe the changes of the responses by changing the parameters of ECO so as to get some reference basis of selected parameters. Numerical optimal experimental results of standard test functions show that on-line performance and off-line performance are improved by variation of parameters.
The on-line performance and off-line performance are provided to evaluate the performances of Election Campaign optimization (ECO) algorithm used in functions optimization. The relatively between the selected para...
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The on-line performance and off-line performance are provided to evaluate the performances of Election Campaign optimization (ECO) algorithm used in functions optimization. The relatively between the selected parameters of ECO and the algorithm performance is analyzed by researching the numerical optimal problem of standard test functions. It is expected to observe the changes of the responses by changing the parameters of ECO so as to get some reference basis of selected parameters. Numerical optimal experimental results of standard test functions show that on-line performance and off-line performance are improved by variation of parameters.
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