Prof. Ku Ruhana Bt Ku M.

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Prof. Ku Ruhana Bt Ku M.

Universiti Utara Malaysia, Malaysia

Prof. Ku Ruhana Bt Ku M.

Ku Ruhana Ku-Mahamud received her BSc in Mathematical Sciences and MSc and PhD in Computer Science. She is a Professor of Computer Science at Universiti Utara Malaysia, Malaysia. She is also the Head of Data Science Research Lab. She is currently the Dean of Awang Had Salleh Graduate School for the College of Arts and Sciences. Her research interests lie in swarm intelligence algorithm, wireless sensor network, pattern recognition and vehicle routing problem.

Title: Hybrid swarm intelligence algorithms for optimization problems

Computational intelligence and metaheuristic 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 metaheuristics by combining different metaheuristics which will benefit from the individual advantages of each method. An effective approach consists in combining a population-based method with a single-solution method (often a local search procedure such as Taboo search with ant colony optimization (ACO)). In hybrid optimization algorithms, many combinations of famous optimization methods have been developed, such as, a hybrid grey wolf optimizer and genetic algorithm, hybrid Cuckoo Search and Particle Swarm Optimization (PSO), a hybrid PSO and ACO and a Hybrid ACO and artificial bee colony algorithm. Hybrid SI-based metaheuristics 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 metaheuristics.