Komodo Mlipir Algorithm

Suyanto, Suyanto; Ariyanto, Alifya Aisyah; Ariyanto, Alifya Fatimah

doi:10.1016/j.asoc.2021.108043

Cited by 67 publications

(100 citation statements)

References 32 publications

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“…KMA is a metaheuristic algorithm that is inspired by the behavior of Komodo dragon [20]. Komodo dragon is a kind of monitor lizard that lives mostly on Komodo island, Indonesia.…”

Section: Related Workmentioning

confidence: 99%

“…In KMA, the population of Komodo is divided into three types or groups: big male, small male, and female [20]. The main role of the big male is conducting intensification with certain diversification.…”

Section: Related Workmentioning

confidence: 99%

“…There are two forces or circumstances conducted to the big male: attraction and distraction [20]. Attraction means a big male is attracted to other big males.…”

Section: Related Workmentioning

confidence: 99%

“…There are two actions that can be chosen by female Komodo in every iteration: mating with the best big male or asexual reproduction (parthenogenesis) [20]. The mating process is like cross-over in genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In general, the small male follows the big male [20]. This process is conducted by accumulating vectors between the small male and all the big males with a certain speed.…”

Section: Related Workmentioning

confidence: 99%

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Stochastic Komodo Algorithm

2022

IJIES

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A novel metaheuristic algorithm is proposed in this paper, namely stochastic komodo algorithm (SKA). This proposed algorithm is an improved version of Komodo mlipir algorithm (KMA), which is inspired by the behaviour of Komodo during foraging and mating. The improvement is conducted by simplifying the basic form of KMA. Like KMA, it consists of three types of Komodo: big male, female, and small male. Male Komodo focuses on intensification. On the other side, females conduct diversification or intensification based on the search space radius. It eliminates sorting mechanism at the beginning of the iteration. Rather than determined from the quality (fitness score), the distribution of the types of Komodo is conducted stochastically at the beginning of every iteration. This proposed algorithm is then tested by using ten functions. Five functions are unimodal, while the five others are multimodal. The proposed algorithm is also compared with several well-known algorithms: football game-based optimization, hide objects game optimization, cloud-theory-based simulated annealing, harmony search, and KMA. The result shows that this proposed algorithm is very competitive compared with these benchmark algorithms in both unimodal and multimodal functions. A female-dominant formation is proven to achieve optimal result.

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“…KMA is a metaheuristic algorithm that is inspired by the behavior of Komodo dragon [20]. Komodo dragon is a kind of monitor lizard that lives mostly on Komodo island, Indonesia.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…There are two forces or circumstances conducted to the big male: attraction and distraction [20]. Attraction means a big male is attracted to other big males.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In general, the small male follows the big male [20]. This process is conducted by accumulating vectors between the small male and all the big males with a certain speed.…”

Section: Related Workmentioning

confidence: 99%

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Stochastic Komodo Algorithm

2022

IJIES

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Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers

Zhong,

Li,

Meng

et al. 2024

Neural Comput & Applic

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An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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Komodo Mlipir Algorithm

Cited by 67 publications

References 32 publications

Stochastic Komodo Algorithm

Stochastic Komodo Algorithm

Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

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