Heuristics mutation operator and local search scheme are designed in the algorithm.
该算法采用新的启发式变异算子和局部搜索算子。
It adopts 2-interchange mutation operator in ant colony algorithm to increase its ability of local search and to improve the quality of the solution.
该算法通过在蚁群算法中引入遗传算法的2 -交换变异算子，增强了算法的局部搜索能力，提高了解的质量。
The algorithm includes selection, expansion and mutation operations, expansion is used for local search and mutation is used for global search in solution space.
算法包括选择、扩展和突变操作，扩展和突变操作分别在解空间中局部和全局范围内搜索最优解。
The multi-objective line search operator interacts with selected operator, crossover operator and mutation operator, making global searching and local searching be well actualized.
线搜索算子与遗传算法中的选择算子、交叉算子和变异算子共同作用，使全局搜索和局部搜索都能够很好的实现。
The Elitist model is utilized to ensure the stable convergence, and the Gaussian mutation operator is used to enhance the local search ability around every peak value.
采用最优保存策略和高斯变异算子，保证算法的稳定收敛和提高算法在每个峰值附近的局部搜索能力。
It introduces the natural number coding method, adaptive probabilities of crossover and mutation, and furthermore, makes use of heuristic information to improve search efficiency effectively.
算法采用自然数编码，自适应的交叉变异算子，并融入启发式信息有效地提高了搜索效率。
Independent mutation is applied to the optimal individual in each generation to improve the search efficiency.
对每一代的最优个体进行单独变异，使搜索效率提高。
Genetic algorithms (GAs) are search algorithms based on of natural evolution processing including selection, mutation and crossover operations on the genes of individuals or potential solutions.
遗传算法是一种借鉴生物界自然选择和自然进化机制的搜索方法，通过对个体进行复制、交叉、变异操作完成搜索过程。
Hybrid crossovers and intermittent mutation are applied to increase the search capability of the algorithm.
采用混合杂交和间歇变异来提高算法的搜索能力。
After every basic mutation, crossover and competition, a new competition with a random swarm is added so as to effectively jump out of the local optimum and enhance the ability of global search.
在每一代变异、交叉和竞争之后，又增加了与随机新种群的竞争操作，使算法易于跳出局部最优点，以提高全局搜索能力。
After every basic mutation, crossover and competition, a new competition with a random swarm is added so as to effectively jump out of the local optimum and enhance the ability of global search.
在每一代变异、交叉和竞争之后，又增加了与随机新种群的竞争操作，使算法易于跳出局部最优点，以提高全局搜索能力。
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