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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 paper deals with a new evolutionary algorithm - Differential Migration, and provides comparison with other algorithms of this type. Cluster Restarted Differential Migration is examined with standard benchmark test functions for performance comparison. Standard Differential Migration and Restart Covariance Matrix Adaptation Evolution Strategy With Increasing Population Size (IPOP-CMA-ES) are used...
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 presents a modified fruit fly optimization algorithm(FOA). The proposed modified FOA establishes a balanced tradeoff between exploration and exploitation, and thus overcomes original FOA's drawbacks of premature convergence and easy trapping in a local optima. In the proposed modified FOA, firstly, the whole population performs a global search; Secondly, the whole population are sequenced...
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