早熟收敛问题是遗传算法中影响寻优效果的重要因素。
Premature convergence is an important factor affecting optimization results in genetic algorithms (GA).
粒子群优化(PSO)算法对于多峰搜索问题一直存在早熟收敛问题。
Particle Swarm Optimization (PSO) algorithm has existed premature convergence for multimodal search problems.
多峰值函数优化结果表明，该算法可以有效地解决早熟收敛问题，更易达到全局最优解。
The optimization result of multi-peak value function shows that the algorithm presented can solve premature convergence problem effectively and converge to the globally optimal solution.
该算法具有很强的全局搜索能力，寻优效率高，有效克服了传统遗传算法的早熟收敛问题。
This algorithm characterizes with global search ability, efficiency in search and avoiding premature phenomena which often occurs in traditional GA.
实验结果表明，该算法具有较快的收敛速度和较高的收敛精度，能有效避免早熟收敛问题。
Experimental results show that the proposed algorithm not only has great advantages of convergence property over standard PSO, but also avoids effectively being trapped in local optimum.
对几种典型函数的测试结果表明，新算法的全局搜索能力有了显著提高，并且能够有效避免早熟收敛问题。
The experimental results show that the new algorithm not only has great advantage of convergence property over PSO, but also can avoid the premature convergence problem effectively.
通过这些改进措施，能够较好的解决SGA的“早熟”收敛和“欺骗”问题。
With these improvements, it can solve the "premature" convergence and "cheating" problem of SGA.
针对差分进化(DE)算法收敛早熟与计算效率不理想的问题，提出一种改进的差分进化算法。
An improved Differential Evolution (DE) algorithm was proposed to solve the problem of premature convergence and improve the computational efficiency of DE.
然而其遗传操作繁杂、计算量庞大、早熟收敛等问题使其应用受到局限。
However, the utilization of Genetic Algorithm in practice was limited due to complicated operation, huge calculating and immature convergence.
在解决像旅行商这类组合优化中的NP完全问题，是极易陷入早熟收敛，城市规模越大越难求得最优解。
GA easily traps in permutation for (NP-hard) problem such as TSP, especially when the city scale is large.
在解决像旅行商这类组合优化中的NP完全问题，是极易陷入早熟收敛，城市规模越大越难求得最优解。
GA easily traps in permutation for (NP-hard) problem such as TSP, especially when the city scale is large.
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