Numerical simulation and animation of a dolphin swarm optimization for the detection and hunting of intruders in a 3d environment

Johan, Siti Aishah (2022) Numerical simulation and animation of a dolphin swarm optimization for the detection and hunting of intruders in a 3d environment. [Project Paper] (Submitted)

Text Siti Aishah Binti Johan.pdf Download (1MB)

Abstract

The analysis of swarm intelligence algorithms has been successfully implemented to several optimization algorithms as a problem-solving strategy for problems that are difficult to solve using standard methods. There are many well-implemented algorithms available now, including particle swarm optimization, genetic algorithms, artificial bee colony algorithms, and ant colony optimization. Those algorithms have already proven promising results. However, as objects become more complicated, it becomes extremely difficult for algorithms to match human demands and expectations of prediction accuracy. Furthermore, there is an urgent need for a security system to detect various malicious threats, as humans have already entered a technological period in which information and data interchange is distributed all around community, including personal data. This intrusion detection-based system can be combined with various numerical solution techniques to improve their performance, and Dolphin Swarm Optimization is one such approach because dolphins have many notable biological traits and living habits including echolocation, information exchanges, cooperation, and division of labour, which can then be combined with swarm intelligence and brought into optimization problems. There are also numerous descriptions of the algorithm's four pivotal phases, which are the searching phase, the call phase, the receiving phase, and the predation phase. Dolphin swarm optimization can be used to enable efficient and accuracy for identifying and hunting intruders by combining biological traits of marine mammals known as dolphins with swarm intelligence. Security frameworks are evaluated with different optimization algorithms in terms of parameters such as false positive, detection rate, detection time, and so on, and it is determined which optimization technique performs better. Furthermore, the dolphin swarm method, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm are used to test ten benchmark functions with diverse features. To demonstrate the influence of the dolphin swarm algorithm, the convergence rates and benchmark function outputs of these four methods are compared. The results reveal that the dolphin swarm method outperforms the other algorithms in most circumstances. The dolphin swarm algorithm has some excellent characteristics, including first-slow-then-fast convergence, periodic convergence, local-optimum-freeness, and no specific need on benchmark functions. Furthermore, the dolphin swarm technique is well suited to optimization problems involving more fitness function calls and fewer people.

Item Type:	Project Paper
Faculty:	Fakulti Sains
Depositing User:	Ms Emelda Mohd Hamid
Date Deposited:	10 Jun 2024 03:48
Last Modified:	10 Jun 2024 03:48
URI:	http://psaspb.upm.edu.my/id/eprint/1805

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