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In primate life, there are a number of various social behavior, such as communication among members in a group, and food sharing, which are vital to maintain their survival. Similar to those of Swarm Intelligence, such as ant colony optimization, the behavior of primates motivates us to develop an algorithm with the aim of solving continuous problems. Our algorithm is inspired by the behavior of the...
Particle Swarm Optimizer is a swarm intelligent algorithm which simulates the behaviour of bird's flocking and fish schooling. This paper presents an improved opposition based Particle Swarm Optimizer. In the proposed method, generalized opposition based learning is incorporated first in population initialization and particle's personal best position. Second, a controlled mechanism of exploration...
The present study, proposes an optimization algorithm for solving the continuous global optimization problems. The basic framework selected for modeling the algorithm is Artificial Bee Colony (ABC). The proposed variant is called ABC with changing factor or CF-ABC. The proposed CF-ABC tries to maintain a tradeoff between exploration and exploitation so as to obtain reasonably good results. The proposed...
This paper introduces an improved artificial colony algorithm. In the algorithm, a new search iteration operator based on the fixed point theorem of Contractive Mapping in Banach Spaces is proposed. In this work, the improved artificial colony algorithm is used to 10 multivariable benchmark functions. The simulation results show that the algorithm possesses an excellent performance in the global optimization,...
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