Fireworks algorithm (FWA) is a novel swarm intelligence algorithm recently proposed for solving complex optimization problems. Because of its powerful global optimization ability to solve classification problems, we f...
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Fireworks algorithm (FWA) is a novel swarm intelligence algorithm recently proposed for solving complex optimization problems. Because of its powerful global optimization ability to solve classification problems, we first present an optimization classification model in this paper. In this model, a linear equation set is constructed according to classification problems. This optimization classification model can be solved by most evolutionary computation techniques. In this paper, a self-adaptive FWA (SaFWA) is developed so that the optimization classification model can be solved efficiently. In SaFWA, four candidate solution generation strategies (CSGSs) are employed to increase the diversity of solutions. In addition, a self-adaptive search mechanism has also been introduced to use the four CSGSs simultaneously. To extensively assess the performance of SaFWA on solving classification problems, eight datasets have been used in the experiments. The experimental results show that it is feasible to solve classification problems through the optimization classification model and SaFWA. Furthermore, SaFWA performs better than FWA, FWA variants with only one CSGS, particle swarm optimization, and differential evolution on most of the training sets and test sets.
The fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II) is currently the most popular multi-objective evolutionaryalgorithm (MOEA). NSGA-II has been shown to work well for two-objective problems by ...
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The fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II) is currently the most popular multi-objective evolutionaryalgorithm (MOEA). NSGA-II has been shown to work well for two-objective problems by attaining near-optimal diverse and uniformly distributed Pareto solutions. To use the powerful multi-objective optimisation performance of NSGA-II directly and conveniently, an optimisation classification model is presented. In the optimisation classification model, a linear equation set is constructed according to classification problems. In this paper, we introduced NSGA-II to solve the optimisation classification model. Besides, eight different datasets have been chosen in experiments to test the performance of NSGA-II. The results show that NSGA-II is able to find much better spread of solutions and has high classification accuracy and robustness.
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