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This paper presents an improved version of the recently proposed Self Regulating Particle Swarm Optimization (SRPSO) algorithm referred to as improved Self Regulating Particle Swarm Optimization (iSRPSO) algorithm. In the iSRPSO algorithm, the last two least performing particles are observed with different perception and they adopt a different learning strategy for velocity update. These particles...
This paper presents a new particle swarm optimization (PSO) algorithm incorporating the concept of mentoring in the learning process for finding the optimum solution, referred to as a Mentoring based Particle Swarm Optimization (MePSO) algorithm. In human learning principles, mentoring provides both self and social cognizance through guidance, direction and momentum for the learners. Such a mentoring...
This paper presents a human meta-cognition inspired search based optimization algorithm, referred to as a Human Meta-cognition inspired Collaborative Search algorithm for optimization problems (HMICSO). Meta-cognition enables self-regulation and collaboration for effective learning and problem solving skills. Meta-cognition has been successfully applied in machine learning algorithms for providing...
In a fully complex-valued feed-forward network, the convergence of the complex-valued back-propagation learning algorithm depends on the choice of the activation function, minimization criterion, initial weights and the learning rate. The minimization criteria used in the existing learning algorithms do not approximate the phase well in complex-valued function approximation problems. This aspect is...
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