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Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are well known optimization tools. PSO advantage is its capability for fast convergence to the promising solutions. On the other hand GAs are able to process schemata thanks to the use of crossover operator. However, both methods have also their drawbacks - PSO may fall into the trap of preconvergence, while GA capability of fast finding...
Particle Swarm Optimization (PSO) is a powerful heuristic optimization method being subject of continuous interest. Theoretical analysis of its properties concerns primarily the conditions necessary for guaranteeing its convergent behaviour. Particle behaviour depends on three groups of parameters: values of factors in a velocity update rule, initial localization and velocity and fitness landscape...
Clustering is a significant data mining task which partitions datasets based on similarities among data. In this study, partitional clustering is considered as an optimization problem and an improved ant-based algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), is applied to automatic grouping of large unlabeled datasets. The proposed algorithm employs Opposition-Based...
This paper proposes a new solution for Traveling Salesman Problem (TSP) using genetic algorithm. A combinational crossover technique is employed in the search for optimal or near-optimal TSP solutions. It is based upon chromosomes that utilise the concept of heritable building blocks. Moreover, generation of a single offspring, rather than two, per pair of parents, allows the system to generate high...
One of the classical problem in dynamics and control theory, which has being widely used as a benchmark for testing control algorithms, such as Linear Quadratic Regulator (LQR) is the balancing of inverted pendulum. The performance of LQR depends largely on the design choice of state and control weighting matrices (Q & R). However, these matrices are usually selected by the designer through a...
Microbial Genetic Algorithm (MGA) is a simple variant of genetic algorithm and is inspired by bacterial conjugation for evolution. In this paper we have discussed and analyzed variants of this less exploited algorithm on known benchmark testing functions to suggest a suitable choice of mutation operator. We also proposed a simple adaptive scheme to adjust the impact of mutation according to the diversity...
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