This study explores a novel application of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) heuristic methods in a hybrid construction on a 4 cylinder medium-duty diesel engine at part-load conditions. The application of the hybrid PSO-GA approach is compared with a basic PSO in the optimization of the control parameters of a diesel engine utilizing high EGR capability, modestly...

In this paper, an effective hybrid algorithm based on Particle Swarm Optimization (PSO) is proposed for solving the Traveling Salesman Problem (TSP), which is a well-known NPcomplete problem. The hybrid algorithm combines the high global search efficiency of fuzzy PSO with the powerful ability to avoid being trapped in local minimum. In the fuzzy PSO system, fuzzy matrices were used to represen...

The Artificial Bee Colony Algorithm (ABC) is a heuristic optimization method based on the foraging behavior of honey bees. It has been confirmed that this algorithm has good ability to search for the global optimum, but it suffers from the fact that the global best solution is not directly used, but the ABC stores it at each iteration, unlike the particle swarm optimization (PSO) that can direc...

Inspired by the estimation capability of Kalman filter, we have recently introduced a novel population-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, which includes prediction, measurement, and estimation, the global minimum/maximum can be estimated. Measurement process, which is r...

A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algorithm is compared to a human-designed PSO algorithm by using ten artifi...

Particle swarm optimization (PSO) has shown its good search ability in many optimization problems. However, PSO often easily fall into local minima because the particles could quickly converge to a position by the attraction of the best particles. Under this circumstance, all the particles could hardly be improved. This paper presents a hybrid PSO, namely LSPSO, to solve this problem by employi...

This paper investigates into hybridization between PSO and self-adaptive evolutionary programming techniques for solving economic dispatch (ED) problem with non-smooth cost curves where conventional gradient based methods are in-applicable. The convergence capability of evolutionary programming technique is enhanced with hybridization of self-adaptive evolutionary programming technique with PSO...

This paper studies a facility location model with fuzzy random parameters and its swarm intelligence approach. A Value-at-Risk (VaR) based fuzzy random facility location model (VaR-FRFLM) is built in which both the costs and demands are assumed to be fuzzy random variables, and the capacity of each facility is unfixed but a decision variable assuming continuous values. Under this setting, the V...

This paper integrates Nelder–Mead simplex search method (NM) with genetic algorithm (GA) and particle swarm optimization (PSO), respectively, in an attempt to locate the global optimal solutions for the nonlinear continuous variable functions mainly focusing on response surface methodology (RSM). Both the hybrid NM–GA and NM–PSO algorithms incorporate concepts from the NM, GA or PSO, which are ...

Recently, Particle Swarm Optimization (PSO) has evolved as a promising alternative to the standard backpropagation (BP) algorithm for training Artificial Neural Networks (ANNs). PSO is advantageous due to its high search power, fast convergence rate and capability of providing global optimal solution. In this paper, the authors explore the improvements in forecasting accuracies of feedforward a...