Hybrid Particle Swarm Genetic Algorithm Research Articles

Launch vehicle multi-objective reliability-redundancy optimization using a hybrid genetic algorithm-particle swarm optimization

This paper focuses on multi-objective reliability optimization of a two-stage launch vehicle using a hybridized Genetic Algorithm-Particle Swarm Optimization with provisions of relative weighting between the objectives. In this respect, the launch vehicle key subsystems as well as their functions are initially introduced. Subsequently, the system reliability block diagram is constructed using the launch vehicle working order of the subsystems augmented with the requirements for a robust fault/failure tolerant design and performance. Next, based on the proposed reliability block diagram arrangement a bi-objective optimization is formulated to maximize the system reliability while minimizing the launch vehicle cost with design variables being the component reliability, system redundancy level, and the type of components. To examine the validity of the proposed algorithm, a benchmark problem in reliability-redundancy optimization is also investigated through the proposed scheme. Benchmark simulation results indicate that neither genetic algorithm nor the particle swarm optimization alone can produce sufficiently accurate optimal results, but their hybrid combination is more efficient and achieves the desired level of accuracy. Thus the proposed hybrid genetic algorithm-particle swarm optimization is applied to the problem of launch vehicle multi-objective reliability optimization that has resulted in a convex Pareto front, out of which many design points could be selected for implementation. Finally, a Monte Carlo simulation is performed against the subsystem reliability uncertainty in order to analyze the effect of uncertainty on the total launch vehicle reliability and cost.

A New Approach for Finding Real Power Generation Direction to Improve Voltage Stability Margin

In this study, an efficient approach is proposed to find best real power generation participation for improving static voltage stability and reducing total generation cost. Therefore, loadability limit index is used to assess static voltage stability security margin, which is associated to the point of voltage collapse. Based on it, a toolbox is developed to recognize the loadability margin by using Lagrangian method. Finally, the mentioned problem is modeled as a non-linear and multiobjective optimization problem. In this paper, Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) and Particle Swarm Optimization (PSO) algorithms are applied to find out best generation direction. This problem is performed as a hierarchical optimization problem. In first stage, an algorithm is used to search possible solution area and reach tradeoff between secure and economic direction of real power generations. In second stage of proposed approach, respectively to each generation pattern, power system voltage stability margin and generation cost are evaluated during search algorithm. The simulations are performed on IEEE 14 and IEEE 30 bus test systems to find best generation direction.

Robotic path planning using hybrid genetic algorithm particle swarm optimisation

The problem of robotic path planning has always attracted the interests of a significantly large number of researchers due to the various constraints and issues related to it. The optimisation in terms of time and path length and validity of the non-holonomic constraints, especially in large sized maps of high resolution, pose serious challenges for the researchers. In this paper we propose hybrid genetic algorithm particle swarm optimisation (HGAPSO) algorithm for solving the problem. Diversity preservation measures are introduced in this applied evolutionary technique. The novelty of the algorithm is threefold. Firstly, the algorithm generates paths of increasing complexity along with time. This ensures that the algorithm generates the best path for any type of map. Secondly, the algorithm is efficient in terms of computational time which is done by introducing the concept of momentum-based exploration in its fitness function. The indicators contributing to fitness function can only be measured by exploring the path represented. This exploration is vague at start and detailed at the later stages. Thirdly, the algorithm uses a multi-objective optimisation technique to optimise the total path length, the distance from obstacle and the maximum number of turns. These multi-objective parameters may be altered according to the robot design.

Fuzzy Enabled Hybrid Genetic Algorithm–Particle Swarm Optimization Approach to Solve TCRO Problems in Construction Project Planning

AbstractOne of the most challenging tasks of a construction project planner is to simultaneously minimize the total project cost and total project duration while considering issues related to optimal resource allocation and resource leveling. Therefore, project planners face complicated multivariate, time-cost-resource optimization (TCRO) problems that require time-cost-resource tradeoff analysis. The hybrid GA–PSO approach is presented to solve complex, TCRO problems in construction project planning. The proposed approach uses the fuzzy set theory to characterize uncertainty about the input data (i.e., time, cost, and resources required to perform an activity) in this hybrid approach. The proposed fuzzy-enabled hybrid GA–PSO approach is applied to solve two optimization problems that are found in the construction project planning literature. It is shown that the proposed fuzzy enabled hybrid GA–PSO approach is superior to existing optimization algorithms at finding better project schedule solutions with ...

A Novel Method for Segmentation of Remote Sensing Images based on Hybrid GAPSO

segmentation is defined as the process of dividing an image into disjoint homogenous regions and it could be regarded as the fundamental step in various image processing applications. In this paper, a novel multilevel thresholding segmentation method is proposed for grouping the pixels of remote sensing (RS) images into different homogenous regions. In this way, Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) is used for finding the optimal set of threshold values. The new method is tested on two different study areas and results are compared with PSO-based image segmentation comprehensively. Results show HGAPSO based image segmentation performs better than PSO-based method in different points of view.