Lion algorithm: Overview, modifications and applications

Saman M. Almufti
Computer Science, Nawroz University, Kurdistan-Region, Iraq
Corresponding Email: [email protected]

A B S T R A C T
The increasing complexity of real-world problems motivated computer scientists and researchers to seek more efficient problem-solving strategies. Because of their ability to adapt to a wide range of conditions, Natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation, and Swarm Intelligence algorithms have been widely used for solving complex, real-world optimization problems. This paper presents a swarm based algorithm that is based on the cooperative behaviors between Loin’s, it is called Lion Algorithm (LA) algorithm. This paper provides a review of these algorithms, with a particular emphasis on the Lion algorithm. Lion Algorithm is based on lions’ unique social behavior, which makes them the world’s strongest animal. Loin Algorithm, like Genetic Algorithm, includes generation, mutation, crossover, and so on. The lion’s territorial defense and territorial takeover behavior distinguishes this algorithm from others.

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Grey wolf optimizer: Overview, modifications and applications

Saman M. Almufti, Hawar B. Ahmad, Ridwan B. Marqas, Renas R. Asaad
Nawroz University, Kurdistan-Region, Iraq  
Corresponding email: [email protected]

A B S T R A C T
The complexity of real-world problems motivated researchers to innovate efficient problem-solving techniques. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization and Non-deterministic polynomial hard (NP-Hard) problems because of their ability to adjust to a variety of conditions. This paper describes Grey Wolf Optimizer (GWO) as a Swarm Based metaheuristic algorithm inspired by the leadership hierarchy and hunting behavior of the grey wolves for solving complex and real-world optimization problems. Since the appearance of GWO many modifications for improving the performance of the algorithm and have been applied to various applications in several fields. At the end of this paper, the improvements are listed.

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