in this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classifypatterns of different classes in the feature space. the introduced mutation operators and chaotic sequences allows us to overcomethe problem of early convergence into a local minima associated with particle swarm optimization algorithms. that is, the mutationope...

The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have...

This paper presents a combinational quantum-inspired binary gravitational search algorithm (QBGSA) for solving the optimal power quality monitor (PQM) placement problem in power systems for voltage sag assessment. In this algorithm, the standard binary gravitational search algorithm is modified by applying the concept and principles of quantum behaviour as to improve the search capability with ...

Binary optimization problems are in the most case the NP-hard problems that call to satisfy an objective function with or without constraints. Various optimization problems can be formulated in binary expression whither they can be resolved in easier way. Optimization literature supplies a large number of approaches to find solutions to binary hard problems. However, most population-based algor...

Many important problems in engineering management can be formulated as Resource Assignment Problem (RAP). The Workers (WAP) is considered a sub-class of RAP which aims to find an optimal assignment workers number tasks order optimize certain objectives. WAP NP-hard combinatorial optimization problem. Due its importance, several algorithms have been developed solve it. In this paper, it that man...