The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The classification is done using DGA data from 60966 bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. PSO and GA were compared in terms of accu...

The premature convergence of genetic algorithms (GAs) is the most major factor of slow evolution of GAs. In this paper we propose a novel method to solve this problem through competition of multiple offspring of individuals. Unlike existing methods, each parents in our method generates multiple offspring and then generated multiple offspring compete each other, finally winner offspring become t...

Exploration and exploitation are the two main concepts of success for searching algorithms. Controlling exploration while executing search algorithm will enhance overall performance algorithm. usually controlled offline by proper settings parameters that affect population-based performance. In this paper, we proposed a dynamic controller one most well-known algorithms, which is Genetic Algorith...

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic ...

Quad rotor is a renowned underactuated Unmanned Aerial Vehicle (UAV) with widespread military and civilian applications. Despite its simple structure, the vehicle suffers from inherent instability. Therefore, control designers always face formidable challenge in stabilization and control goal. In this paper fuzzy membership functions of the quad rotor’s fuzzy controllers are optimized using nat...

Genetic algorithms are powerful search algorithms that can be applied to a wide range of problems. Generally, parameter setting is accomplished prior to running a Genetic Algorithm (GA) and this setting remains unchanged during execution. The problem of interest to us here is the self-adaptive parameters adjustment of a GA. In this research, we propose an approach in which the control of a gene...

Constraint satisfaction problems (CSP) are widely studied in a large area of computer sciences, and many problems can be treated as CSPs. Furthermore, many solving methods exist such as complete methods, local search (for example tabu search) and genetic algorithms (GA) [4]. The goal of this paper is to show that for some graph coloring problems, especially over-constrained, using genetic algor...

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, con...

In an Open-Pit Production Scheduling (OPPS) problem, the goal is to determine the mining sequence of an orebody as a block model. In this article, linear programing formulation is used to aim this goal. OPPS problem is known as an NP-hard problem, so an exact mathematical model cannot be applied to solve in the real state. Genetic Algorithm (GA) is a well-known member of evolutionary algorithms...

Efficient parallelization of genetic algorithms (GAs) on state-of-the-art multi-threading or many-threading platforms is a challenge due to the difficulty of schedulation of hardware resources regarding the concurrency of threads. In this paper, for resolving the problem, a novel method is proposed, which parallelizes the GA by designing three concurrent kernels, each of which running some depe...