Abstract
In various research areas there is a requirement for optimized results in various research-oriented parameters. In steganography, the important task is to find out the areas, where we can hide our secret image. This search can be done through optimization techniques. Various other parameters related to image processing research areas like peak signal to noise ratio, compression ratio, etc. can be optimized using various optimization techniques. Optimization techniques are parts of artificial intelligence. This paper provides a brief review of various optimization algorithms with implementations of the salp swarm optimization algorithm and particle swarm optimization algorithm. Swarm intelligence (SI) is a technique that gives various optimization techniques. These techniques give the optimized results of any research area. Many types of swarm optimization algorithms have evolved, like artificial bee colony algorithm, glowworm swarm optimization, particle swarm optimization, ant colony optimization, and salp swarm optimization algorithm. This paper also gives advantages and disadvantages. The future scope of the swarm optimization algorithm is also discussed.
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Dhawan, S., Gupta, R., Rana, A., Sharma, S. (2021). Various Swarm Optimization Algorithms: Review, Challenges, and Opportunities. In: Marriwala, N., Tripathi, C.C., Jain, S., Mathapathi, S. (eds) Soft Computing for Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-1048-6_22
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DOI: https://doi.org/10.1007/978-981-16-1048-6_22
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