DEVELOPMENT OF A SMELL AGENT OPTIMIZATION ALGORITHM FOR COMBINATORIAL OPTIMIZATION PROBLEMS
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- 1-5 Chapters
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- Recommended for : Student Researchers
- NGN 3000
Excerpt from the Study
Combinatorial optimization involves the mathematical study of arraignment, ordering, selection or grouping of discrete objects. The complexity of combinatorial problem usually increases as the problem dimension becomes very large. Solving these large dimensional problems usually requires an enormous amount of time and computational resources before a combination of discrete solution (which may not even be optimum) is obtained. Over the years, researchers have sought for an efficient means of solving these problems with a common focus on obtaining the best possible combination of results within a short possible interval of time. This quest plays a significant role in the development of already well-established and widely accepted fields of computational intelligence algorithms. Interestingly, these algorithms have appeared to be more efficient (in terms of computation, precision and complexity) in solving various degrees of optimization problems when compared with the traditional methods. Generally speaking, all computational intelligence algorithms were developed through careful observation of various natural phenomena. The majority were focussed only on the foraging behaviours of most natural behaviours of some biological agents as the agents seek to identify the location of food source within its environment. In several situations, the nature and concentration of the food do influence the behaviour of the agent. For example, the movement of fish in water is attracted towards the region of high 12 concentration of food. The implication of this is that, the movement of fish is restricted to only the region of high concentration of food. However, considering only the foraging movement of the agents towards the food source does not truly represent the real-life scenario of a biological system. This leads to the problem of imbalance between exploration and exploitation, increasing the chances of poor convergence and subsequently leading the algorithm into local minima. Considering these limitations and bearing in mind the “no free lunch theorem” this research developed a new optimization algorithm using the natural phenomenon of smell. In the proposed research, the Brownian nature of the smell molecules as they evaporate from the smell source towards the agent is mathematically modelled. Thereafter, the trailing behaviour of the agent towards the smell source after sniffing the smell molecules is also developed. These mathematical models were codified and the resulting bio-inspired optimization algorithm is termed smell agent optimization (SAO). The performances of SAO will be evaluated through comparison with gaseous Brownian motion optimization (GBMO) and fruit fly optimization (FFO) on a total of thirty-nine (39) applied mathematical optimization benchmark functions. After the performance evaluation, the proposed algorithm will be used to develop a model of robot path planning and minimum spanning tree and results were compared with that of particle swarm optimization (PSO) and smell detection agent (SDA) algorithm.
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