A hybrid particle swarm optimization (PSO) for multi-machine time scheduling problem (MTSP) with multicycles is proposed in this paper to choose the best starting time for each machine in each cycle under pre-described time window and a set of precedence machines for each machine; to minimize the total penalty cost. We developed hybrid algorithm by using a combination between PSO and Genetic Al...

This paper presents the use of the improved harmony search method for solving economic load dispatch problems. The harmony search method mimics a jazz improvisation process by musicians in order to seek a fantastic state of harmony. To assess the searching performance of the proposed method, a six-unit thermal generating system acquired from the standard IEEE 30-bus test system was challenged. ...

This paper introduces a Shuffled Frog-Leaping Algorithm based method for the optimization of machining parameters for milling operations. An objective function based on maximum profit in milling operation has been used. The algorithm is compared with others techniques and outperforms the results reached by standard shuffled frogleaping algorithm, differential evolution, particle swarm optimizat...

Naturally occurring phenomenon serves as an unbiased guide for solving various optimization problems. This paper compiles some of the population-based, stochastic optimization algorithms including the recently developed social impact theory based optimizer, SITO. The current state of research, including the natural phenomena followed by each and some of their applications to solve various optim...

In recent years the area of Evolutionary Computation has come into its own. Two of the popular developed approaches are Genetic Algorithms and Particle Swarm Optimisation, both of which are used in optimisation problems. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare th...

Bio-inspired algorithms become among the most powerful algorithms for optimization. In this paper, we intend to provide one of the recent bio-inspired metaheuristic which is the Firefly Algorithm (FF) to optimize power dispatching. For evaluation, we adapt the particle swarm optimization to the problem in the same way as the firefly algorithm. The application is done in an IEEE-14 and on two th...

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle s...

In this paper, a novel Particle Swarm Optimization (PSO) identification algorithm for time-varying systems with a colored noise is presented. Presented criterion function can show not only outside system output error but also inside parameters error in order to explain more difference between actual and estimative system. Identification algorithm may consist of many different PSO algorithms tha...

This paper presents the use of the improved harmony search method for solving economic load dispatch problems. The harmony search method mimics a jazz improvisation process by musicians in order to seek a fantastic state of harmony. To assess the searching performance of the proposed method, a six-unit thermal generating system acquired from the standard IEEE 30-bus test system was challenged. ...

Short term hydrothermal scheduling (STHTS) is a very complex, dynamic large-scale non-linear optimization problem. There are many algorithms and powerful optimization methods used to address this issue. Evolutionary algorithms have been effectively employed to obtain a global optimized solution of non linear problems like STHTS. Particle Swarm Optimization (PSO) is an evolutionary method. It ca...