Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches
Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches
复制标题
元启发法、算法和优化方法的发展趋势
DOI：
10.4018/978-1-4666-2145-9
复制
发表时间：
2012
影响因子：
10.4
通讯作者：
Peng
中科院分区：
文献类型：
--
作者：
Peng
元计算学的发展继续推动计算超越其传统方法。以多学科研究成果为基础，元计算、算法和优化方法使用内存操作，以充分利用战略级问题解决。元计算、算法和优化方法的发展趋势提供了元计算计算技术的最新进展和分析。提供广泛覆盖的主题，如遗传算法，差分进化，蚁群优化，这本书的目的是成为一个论坛的研究人员，从业者和学生谁希望学习和应用元启发式计算。
Developments in metaheuristics continue to advance computation beyond its traditional methods. With groundwork built on multidisciplinary research findings; metaheuristics, algorithms, and optimization approaches uses memory manipulations in order to take full advantage of strategic level problem solving.Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches provides insight on the latest advances and analysis of technologies in metaheuristics computing. Offering widespread coverage on topics such as genetic algorithms, differential evolution, and ant colony optimization, this book aims to be a forum researchers, practitioners, and students who wish to learn and apply metaheuristic computing.