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A Brief Review of Nature-Inspired Algorithms for Optimization
Swarm intelligence and bio-inspired algorithms form a hot topic in the d...
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A Social Spider Algorithm for Global Optimization
The growing complexity of real-world problems has motivated computer sci...
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Multimodal Optimization by Sparkling Squid Populations
The swarm intelligence of animals is a natural paradigm to apply to opti...
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Whale swarm algorithm with iterative counter for multimodal function optimization
Most real-world optimization problems often come with multiple global op...
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Handling equality constraints by adaptive relaxing rule for swarm algorithms
The adaptive constraints relaxing rule for swarm algorithms to handle wi...
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SWAF: Swarm Algorithm Framework for Numerical Optimization
A swarm algorithm framework (SWAF), realized by agent-based modeling, is...
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Magnetic Navigation of a Rotating Colloidal Swarm Using Ultrasound Images
Microrobots are considered as promising tools for biomedical application...
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Whale swarm algorithm for function optimization
Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper has proposed a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired by the whales behavior of communicating with each other via ultrasound for hunting. The proposed Whale Swarm Algorithm has been compared with several popular metaheuristic algorithms on comprehensive performance metrics. According to the experimental results, Whale Swarm Algorithm has a quite competitive performance when compared with other algorithms.
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