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Hybrid Swarm Intelligence Algorithms for Optimization Problems


Computational intelligence and meta-heuristic algorithms have become increasingly popular in computer science, artificial intelligence, machine learning, engineering design, data mining, image processing, and data-intensive applications. Several algorithms in computational intelligence and optimization are developed based on swarm intelligence (SI). Different algorithms may have different features and thus may behave differently, even with different efficiencies. However, It still lacks in-depth understanding why these algorithms work well and exactly under what conditions. The current trend is to design hybrid SI-based meta-heuristics by combining them with other SI-based meta-heuristics or other methods, which will benefit from the individual advantages of each method. An effective approach is to combine a single SI-based algorithm with a single-solution method which is often a local search procedure such as Taboo search. Many hybridization of famous SI-based optimization algorithms have been developed, such as, hybrid grey wolf optimizer and genetic algorithm, hybrid Cuckoo Search and Particle Swarm Optimization (PSO), hybrid PSO and Ant Colony Optimization (ACO), and Hybrid ACO and artificial bee colony algorithm. Hybrid SI-based meta-heuristics can obtain satisfying results when solving optimization problems in a reasonable time. However, they suffer especially with high-dimensional optimization problems. Future research to overcome this limit could be in the area of parallel meta-heuristics.

Speaker Profile

Prof. Dr. Ku Ruhana Ku Mahamud
Dean, Graduate School, Universiti Utara Malaysia
Head, Data Science Research Lab., UUM
Adjunct Professor, Shibaura Institute of Technology, Tokyo, Japan
Visiting Professor, Universitas Muslim Nusantara Al-Washliyah, Medan, Indonesia
Visiting Professor, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia