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This paper proposed an improved gra
This paper proposed an improved grasshopper algorithm. It is successfully applied to global optimization problems. GOA is recently proposed metaheuristic algorithm inspired by the swarming behavior of the grasshoppers. However, the original GOA has some drawbacks, such as slow convergence speed, easy to fall into local local optimum and other problems. To overcome these shortcomings, this paper proposes a grasshopper algorithm (CCGOA) based on logistic chaos opposition-based learning strategy and inertia weight of cloud model. CCGOA is divided into three stages. In the first stage, the cha-os reverse learning initialization strategy is used to initialize the population, so that the population can be evenly distrib-uted in the feasible solution space as much as possible, so as to improve the uniformity and diversity of the initial popula-tion distribution of locust algorithm. In the second stage, inertia weight of cloud model is introduced into locust algo-rithm, and different inertia weight strategies are used to adjust the convergence speed of algorithm. In the third stage, based on the principle of Logistic chaotic mapping, local depth search is carried out to reduce the probability of falling into local optimum. 14 benchmark functions and an engineering example are used for simulation verification. Experi-mental results show that the proposed CCGOA algorithm has superior performance in determining the optimal solution of the test function problem.
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This paper proposed an improved grasshopper algorithm. It is successfully applied to global optimization problems. GOA is recently proposed metaheuristic algorithm inspired by the swarming behavior of the grasshoppers. However, the original GOA has some drawbacks, such as slow convergence speed, easy to fall into local local optimum and other problems. To overcome these shortcomings, this paper proposes a grasshopper algorithm (CCGOA) based on logistic chaos opposition-based learning strategy and inertia weight of cloud model. CCGOA is divided into three stages. In the first stage, the cha-os reverse learning initialization strategy is used to initialize the population, so that the population can be evenly distrib-uted in the feasible solution space as much as possible, so as to improve the uniformity and diversity of the initial popula-tion distribution of locust algorithm. In the second stage, inertia weight of cloud model is introduced into locust algo-rithm, and different inertia weight strategies are used to adjust the convergence speed of algorithm. In the third stage, based on the principle of Logistic chaotic mapping, local depth search is carried out to reduce the probability of falling into local optimum. 14 benchmark functions and an engineering example are used for simulation verification. Experi-mental results show that the proposed CCGOA algorithm has superior performance in determining the optimal solution of the test function problem.
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This paper proposed an improved grasshopper algorithm. It is successfully applied to global optimization problems. GOA is recently proposed metaheuristic algorithm inspired by the swarming behavior of the grasshoppers. However, the original GOA has some drawbacks, such as slow convergence speed, easy to fall into local local optimum and other problems. To overcome these shortcomings, this paper proposes a grasshopper algorithm (CCGOA) based on logistic chaos opposition-based learning strategy and inertia weight of cloud model. CCGOA is divided into three stages. In the first stage, the cha-os reverse learning initialization strategy is used to initialize the population, so that the population can be evenly distrib-uted in the feasible solution space as much as possible, so as to improve the uniformity and diversity of the initial popula-tion distribution of locust algorithm. In the second stage, inertia weight of cloud model is introduced into locust algo-rithm, and different inertia weight strategies are used to adjust the convergence speed of algorithm. In the third stage, based on the principle of Logistic chaotic mapping, local depth search is carried out to reduce the probability of falling into local optimum. 14 benchmark functions and an engineering example are used for simulation verification. Experi-mental results show that the proposed CCGOA algorithm has superior performance in determining the optimal solution of the test function problem.
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提出了一种改进的蚱蜢算法它已成功地应用于全局优化问题。GOA算法是受蝗虫群集行为启发而提出的一种元启发式算法然而，原goa算法存在收敛速度慢、易陷入局部最优等问题。针对这些不足，本文提出了一种基于logistic混沌对立学习策略和云模型惯性权重的蚱蜢算法（ccgoa）。CCGOA分为三个阶段。第一阶段采用cha os反向学习初始化策略对种群进行初始化，使种群尽可能均匀地分布在可行解空间中，从而提高蝗虫算法初始种群分布的均匀性和多样性。第二阶段，将云模型的惯性权重引入到蝗虫算法中，采用不同的惯性权重策略来调整算法的收敛速度。第三阶段，基于Logistic混沌映射原理，进行局部深度搜索，降低陷入局部最优的概率采用14个基准函数和一个工程实例进行仿真验证。实验结果表明，所提出的ccgoa算法在确定测试函数问题的最优解方面具有优越的性能。<br>
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