A new algorithm based on Modified Particle Swarm Optimization (MPSO) in order to control autonomous vehicles for solving odor source localization in dynamic advection-diffusion environment have been developed. Furthermore an improvements of the MPSO for odor source localization, which follows a local gradient of the chemical concentration within a plume is investigated. Another popular biomimet...

Hybrid algorithm based on Particle Swarm Optimization (PSO) and Simulated annealing (SA) is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hy...

This paper presents a new approach for solution of the economic load dispatch (ELD) problem with valve-point effect using a modified particle swarm optimization (MPSO) technique. The practical ELD problems have non-smooth cost function with equality and inequality constraints, which make the problem of finding the global optimum difficult when using any mathematical approaches. In this paper, a...

We propose an effective method to design a modified particle swarm optimization MPSO singularity control method for a fully parallel robot manipulator. By adopting MPSO to obtain simple and effective estimated damping values, the result automatically adjusts the damping value around a singular point and greatly improves the accuracy of system responses. This method works by damping acceleration...

Particle Swarm Optimization, a recently more popular optimizer utilizes the swarm behavior in searching for an optimum. The classical/standard PSO (SPSO) algorithm is normally used for unconstrained optimization tasks. The Directional Over-Current Relay (DOCR) Coordination problem is a highly constrained optimization problem, therefore the SPSO in its original form cannot be used to solve it. T...

In this paper, a modified particle swarm optimization (MPSO) algorithm is proposed to solve the reliability redundancy optimization problem. This algorithm modifies the strategy of generating new position of particles. For each generation solution, the flight velocity of particles is removed. Whereas the new position of each particle is generated by using difference strategy. Moreover, an adapt...

The structure optimization of main beam is a nonlinear constrained optimization problem, which is important for bridge crane to save manufacturing cost on quality assurance. The modified particle swarm optimization (MPSO) with feasibility-based rules [1], which was advanced to solve mixed-variable optimization problems, is proposed to optimize the structure of main beam in order to find the opt...

Mutative Particle Swarm Optimization (MPSO) is swarm-based stochastic optimization algorithm combined with the mutative function inspired by Genetic Algorithm (GA). The algorithm searches for the solution by combining swarming behavior, as well as mutation of the particles to accelerate the search process. This paper presents a modification to the MPSO algorithm for it to solve Integer Programm...

A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed, by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC). Firstly, a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC). It helps each swarm member in receiving a multitude of information from other members and thus decreases t...

The main purpose of data mining is to extract hidden predictive knowledge of useful information and patterns of data from large databases for utilizing it in decision support. Medical field has large amount of various heterogeneous databases, in which the extraction of hidden useful knowledge for the classification of data is difficult one. In order to cluster and classify the whole databases o...