Particle swarm optimization for pap-smear diagnosis

Designing simulated annealing and subtractive clustering based fuzzy classifier

“…The result shows that a 98.8% accuracy rate by the SASCFC-Type1 algorithm in testing phase is achieved. This is a reasonable accuracy rate according to the recent works about pap-smear classification problem in literature [47].…”

Solving optimization problems using black hole algorithm

“…Metaheuristic approaches have been formed according to inspiration by nature, physics and human being. In recent years, many of these algorithms and their improved algorithms have been successfully applied to various problems of engineering optimization [12][13][14][15][16]. A common feature in meta-heuristic approaches is that they combine rules and randomness to imitate natural phenomena.…”

“…These algorithms are one of the approximate optimization approaches that have mechanism of departing from local optimum. Many of the meta-heuristic algorithms have been successfully applied to various engineering optimization problems over the recent years [12][13][14][15][16]. In order to get suitable solutions, they have better performance than conventional calculations methods for some complicated and difficult real-world optimization problems.…”

“…A common feature in meta-heuristic approaches is that they combine rules and randomness to imitate natural phenomena. These phenomena include the biological evolutionary process (e.g., the Genetic Algorithm (GA) [17][18] and the Differential Evolution (DE) [12][13]), animal behavior (e.g., Particle Swarm Optimization (PSO) [14] and Ant Colony Algorithm (ACA) [15][16]), and the physical annealing process (e.g., Simulated Annealing (SA) [1,19]). These algorithms are one of the approximate optimization approaches that have mechanism of departing from local optimum.…”

Improved Cuckoo Search Algorithm for Feed forward Neural Network Training

“…Over the last decades, many meta-heuristic algorithms have been successfully applied to various engineering optimization problems [8][9][10][11][12][13][14]. For most complicated real-world optimization problems, they have provided better solutions in comparison with conventional numerical methods.…”