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- Particle swarm optimization (PSO) algorithm is easy to be trapped into local minima in optimizing multimodal function. 摘要针对利用粒子群优化算法进行多极值点函数优化时，存在陷入局部极小点和搜寻效率低的问题。
- This paper presents a genetic algorithm based on sorting in accordance with multimodal function optimization. 本文针对多峰函数优化问题,提出一种基于排序的遗传算法。
- The simulation about optimal problems of multimodal function shows the new algorithm is effective. 对多峰函数的仿真实验表明了算法的有效性。
- Based on the analysis of immune theory and Hopfield neural network(HNN),a new algorithm for multimodal function optimization was proposed. 实验表明,该算法是一种有效的求解多峰函数优化问题的方法,与免疫算法相比,搜索效率和精度都较高。
- A new algorithm for multimodal function optimization was presented by introducing a cluster mechanism in the clonal selection process. 通过在克隆选择过程中引入抗体聚类机制，提出了一种用于复杂多模函数优化的新算法。
- The simulation experiment shows that the MFPSO algorithm possesses clear mechanism and simple operation.The MFPSO algorithm is successful in multimodal function optimization. 仿真实验表明该算法概念清楚，计算简单，具有很好的局部寻优特性，可应用求解于多峰寻优问题。
- Aimed at particle swarm optimization(PSO) algorithm being easily trapped into local minima value in multimodal function,a rotating surface transformation(RST) method was proposed. 针对粒子群优化算法(PSO)应用于多极值点函数易陷入局部极小值,提出旋转曲面变换(RST)方法。
- Aimed at particle swarm optimization (PSO) algorithm being easily trapped into local minima value in multimodal function, a rotating surface transformation (RST) method was proposed. 摘要针对粒子群优化算法(PSO)应用于多极值点函数易陷入局部极小值，提出旋转曲面变换(RST)方法。
- The experiments show that this new algorithm is an effective method for multimodal function optimization.Compared to immune algorithm, the efficiency and precision are both improved. 实验表明，该算法是一种有效的求解多峰函数优化问题的方法，与免疫算法相比，搜索效率和精度都较高。
- Aiming at the problem that genetic algorithm has difficulty in solving multimodal function optimization, an immune network algorithm is proposed based on double mutation operators. 摘要针对遗传算法难以解决多峰函数优化的问题，提出一种基于双变异算子的免疫网络算法。
- Extremum problem of multimodal functions is a difficult issue in optimization fields. 多峰值函数的极值问题一直是优化领域中的一个难点和热点。
- Although very easy to implement, this hybrid SM-PSO is an efficient way to locate global optima of continuous multimodal functions. 算法实现简单，具有很高的可靠性，是一种求解多峰连续函数极值的有效方法。
- Two multimodal functions are optimized with this algorithm.Emulational experimental figures show AMGARC has nice performance. 同时将该算法用来求解多峰值测试函数，实验结果验证了算法的有效性。
- multimodal function optimization 多峰函数优化
- Testing two typical multimodal functions shows that the niche genetic algorithm has more powerful global searching ability and can converge more quickly than the simple genetic algorithm. 用该算法对两个典型多峰函数求解的测试结果表明，该算法较之基本遗传算法有更强的全局搜索能力和更快的收敛速度。
- For multimode function optimization, a quantum SPSO (Q-SPSO) is provided to deal with multiple local optima. 实验分析了采样周期对算法优化行为的影响。
- Application of a Modified Genetic Algorithm in Multimodal Function Optimization 一种改进的遗传算法在多峰值函数优化问题中的应用
- Crowding Clustering Genetic Algorithm for Multimodal Function Optimization 多模态函数优化的拥挤聚类遗传算法
- A constant, variable, function name, or expression. 一种常数、变量、函数名或表达式。
