Assembly sequence planning (ASP) is a combinatorial optimization problem in which the order for each part and subassembly is determined. This order is then incorporated into an incrementally expanding subassembly and eventually results in a final assembly. To address this problem, we propose an improved cat swarm optimization (CSO) algorithm and redefine some basic CSO concepts and operations a...

Efficient multiprocessor scheduling is essentially the problem of allocating a set of computational jobs to a set of processors to minimize the overall execution time. The main issue is how jobs are partitioned in which total finishing time and waiting time is minimized. Minimization of these two criteria simultaneously, is a multi objective optimization problem. There are many variations of th...

Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition...

In this paper, a new improved Differential Search Algorithm optimization approach is proposed and successfully applied to the design and tuning of a PID-type Fuzzy Logic Controller. The scaling factors tuning problem of the Fuzzy Logic Controller structure is formulated and systematically resolved, using the improved constrained Differential Search Algorithm-based method. A comparison, with the...

This paper builds the power transformer diagnosis model based on the improved particle swarm optimization— support vector machine (MPSO-SVM). Speed update and particle self-adaptation self-variation are introduced to optimize the standard particle swarm algorithm, thus overcoming the defect of the standard particle swarm optimization algorithm and increasing the power transformer fault diagnosi...

The dogleg severity is one of the most important parameters in directional drilling. Improvement of these indicators actually means choosing the best conditions for the directional drilling in order to reach the target point. Selection of high levels of the dogleg severity actually means minimizing well trajectory, but on the other hand, increases fatigue in drill string, increases torque and d...

In view of the shortcomings of the test data generation algorithm including particle swarm optimization algorithm and ant colony algorithm, a new algorithm is proposed, which is based on the combination of particle swarm algorithm and parameter adjustment. This algorithm can dynamically adjust its search capabilities based on the fitness value of particles , combine the advantages of particle s...

Back propagation algorithm (BPA) have the complexity, local minima problem so we are using Particle Swarm optimization (PSO) algorithms to reduce and optimize BPA. In this paper, two variants of Particle Swarm Optimization (PSO) PSO_Hill and PSO_A* is used as optimization algorithm. PSO_Hill and PSO_A* algorithms are analyzed and evaluated on the basis of their advantages, applied to feed forwa...

this study proposes a modified version of cultural algorithms (cas) which benefits from rule-based system for influence function. this rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. this is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. this rule ...

So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of i...