F.1. General
Farzad Zandi; Parvaneh Mansouri; Reza Sheibani
Abstract
In the field of optimization, metaheuristic algorithms have garnered significant interest. These algorithms, which draw inspiration from natural selection, evolution, and problem-solving strategies, offer an alternative approach to solving complex optimization problems. Unlike conventional software engineering ...
Read More
In the field of optimization, metaheuristic algorithms have garnered significant interest. These algorithms, which draw inspiration from natural selection, evolution, and problem-solving strategies, offer an alternative approach to solving complex optimization problems. Unlike conventional software engineering methods, metaheuristics do not rely on derivative calculations in the search space. Instead, they explore solutions by iteratively refining and adapting their search process. The no-free-lunch (NFL) theorem proves that an optimization scheme cannot perform well in dealing with all optimization challenges. Over the last two decades, a plethora of metaheuristic algorithms has emerged, each with its unique characteristics and limitations. In this paper, we propose a novel meta-heuristic algorithm called ISUD (Individuals with Substance Use Disorder) to solving optimization problems by examining the clinical behaviors of individuals compelled to use drugs. We evaluate the effectiveness of ISUD by comparing it with several well-known heuristic algorithms across 44 benchmark functions of varying dimensions. Our results demonstrate that ISUD outperforms these existing methods, providing superior solutions for optimization problems.
H.6.4. Clustering
P. Shahsamandi Esfahani; A. Saghaei
Abstract
Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering ...
Read More
Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two contradictory objective functions based on maximum data compactness in clusters (the degree of proximity of data) and maximum cluster separation (the degree of remoteness of clusters’ centers) is proposed. In order to solve this model, a recently proposed optimization method, the Multi-objective Improved Teaching Learning Based Optimization (MOITLBO) algorithm, is used. This algorithm is tested on several datasets and its clusters are compared with the results of some single-objective algorithms. Furthermore, with respect to noise, the comparison of the performance of the proposed model with another multi-objective model shows that it is robust to noisy data sets and thus can be efficiently used for multi-objective fuzzy clustering.
