Artificial Intelligence Based Optimization Techniques: A Review

被引:9
|
作者
Swarnkar, Agrani [1 ]
Swarnkar, Anil [2 ]
机构
[1] Manipal Univ Jaipur, Dept Comp Sci Engn, Jaipur 303007, Rajasthan, India
[2] Malaviya Natl Inst Technol, Dept Elect Engn, Jaipur, Rajasthan, India
关键词
Ant colony optimization; BAT algorithm; Elephant herding optimization; Genetic algorithm; Particle swarm algorithm; ANT COLONY OPTIMIZATION; DISTRIBUTION-SYSTEMS; LOSS REDUCTION; MULTIOBJECTIVE RECONFIGURATION; NETWORK RECONFIGURATION; GENETIC ALGORITHM;
D O I
10.1007/978-981-15-0214-9_12
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Many artificial intelligence based optimization techniques have been introduced since the early 60s. This paper provides a brief review of some of the well-known optimization techniques, e.g., Genetic Algorithm, Particle Swarm Algorithm, and Ant Colony Optimization and recently developed techniques, e.g., BAT Algorithm and Elephant Herding Optimization. All these techniques are population-based search algorithms, in which the initial population is created randomly initializing input parameters within the specified range. They approach toward the best solution inspired by the behavior of natural entities. All of these techniques have a potential to provide optimal or near-optimal solutions.
引用
收藏
页码:95 / 103
页数:9
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