In this paper the benchmarking functions are used to evaluate and check particle swarm optimization (PSO) algorithm. However, utilized have two dimension but they selected with different difficulty models. order prove capability of PSO, it is compared genetic algorithm (GA). Hence, algorithms in terms objective standard deviation. Different runs been taken get convincing results parameters chos...

Automated bioacoustics analysis is being increasingly used to describe environmental phenomena such as species abundance and biodiversity. Within this research area, many algorithms have been proposed. These achieve different sub-objectives within processes can be combined form workflows. However, these are typically evaluated in a limited number of scenarios rarely with combinations other task...

Clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. The aim of clustering is to collect data points. In this paper, a Cultural Algorithm (CA) is presented to optimize partition with N objects into K clusters. The CA is one of the effective methods for searching into the problem space in order to find a n...

Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal Optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to some NP-hard...

Genetic Algorithm (GA), Ant Colony Optimization (ACO) algorithm and Particle Swarm Optimization (PSO) are proposed for feature selection, and their performance is compared. The Spatial Gray Level Dependence Method (SGLDM) is used for feature extraction. The selected features are fed to a three-layer Backpropagation Network hybrid with Ant Colony Optimization and Particle Swarm Optimization (BPN...

Evolutionary computation (EC) techniques such as genetic algorithms (GAs), utilize multiple searching points in the solution space like PSO. Whereas GAs can treat combinatorial optimization problems, PSO was aimed to treat nonlinear optimization problems with continuous variables originally. Moreover, PSO has been expanded to handle combinatorial optimization problems and both discrete and cont...

Yin-Yang-pair optimization (YYPO) is one of the latest metaheuristic algorithms (MA) proposed in 2015 that tries to inspire the philosophy of balance between conflicting concepts. Particle swarm optimizer (PSO) is one of the first population-based MA inspired by social behaviors of birds. In spite of PSO, the YYPO is not a nature inspired optimizer. It has a low complexity and starts with only ...

We generalize Stochastic Local Search (SLS) heuristics into a unique formal model. This model has two key components: common structure designed to be as large possible and parametric intended small possible. Each heuristic is obtained by instantiating the part in different way. Particular instances for Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm (PSO) are presented. T...

This study focuses on the forecasting of energy demands of residential and commercial sectors using linear and exponential functions. The coefficients were obtained from genetic and particle swarm optimization (PSO) algorithms. Totally, 72 different scenarios with various inputs were investigated. Consumption data in respect of residential and commercial sectors in Iran were collected from the ...