Hybrid Genetic Algorithm Particle Swarm Optimization Research Articles

Periodic charging planning for a mobile WCE in wireless rechargeable sensor networks based on hybrid PSO and GA algorithm

Previous studies of periodic charging planning in Wireless Rechargeable Sensor Networks (WRSNs) assumed that the traveling energy of a mobile Wireless Charging Equipment (WCE) has sufficient energy for charging travel and the energy depletion rate at each sensor is identical. These assumptions, however, are not realistic. In fact, the traveling energy of the WCE is limited by the energy capacity of the WCE and the energy consumptions at different sensor nodes are imbalanced. In this paper, a periodic charging planning for a mobile WCE with limited traveling energy is proposed. With the optimization objective of maximizing the the docking time ratio, this periodic charging planning ensures that the energy of the nodes in the WRSN varies periodically and that nodes perpetually fail to die. To deal with the problem, a Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA) is proposed due to the NP-Hard of the problem. Extensive simulations have been conducted, the experimental results indicate that the proposed periodic charging planning can avoid node deaths and keep the energy of sensor nodes varying periodically. Compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), the algorithm HPSOGA outperforms both of these two algorithms empirically.

An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem

Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into genetic algorithm, and therefore, during the process of evolution, the offspring in genetic algorithm can learn the characteristics of elite chromosomes from the bi-memory learning. For solving multi-objective flexible job-shop scheduling problem, this study proposes a discrete particle swarm optimization algorithm. The population is partitioned into two subpopulations for genetic algorithm module and particle swarm optimization module. These two algorithms simultaneously search for solutions in their own subpopulations and exchange the information between these two subpopulations, such that both algorithms can complement each other with advantages. The proposed algorithm is evaluated on some instances, and experimental results demonstrate that the proposed algorithm is an effective method for multi-objective flexible job-shop scheduling problem.

Non-linear Fractional-Order Chaotic Systems Identification with Approximated Fractional-Order Derivative based on a Hybrid Particle Swarm Optimization-Genetic Algorithm Method

Although many mathematicians have searched on the fractional calculus since many years ago, but its application in engineering, especially in modeling and control, does not have many antecedents. Since there are much freedom in choosing the order of differentiator and integrator in fractional calculus, it is possible to model the physical systems accurately. This paper deals with time-domain identification fractional-order chaotic systems where conventional derivation is replaced by a fractional one with the help of a non-integer derivation. This operator is itself approximated by a N-dimensional system composed of an integrator and a phase-lead filter. A hybrid particle swarm optimization (PSO) and genetic algorithm (GA) method has been applied to estimate the parameters of approximated nonlinear fractional-order chaotic system that modeled by a state-space representation. The feasibility of this approach is demonstrated through identifying the parameters of approximated fractional-order Lorenz chaotic system. The performance of the proposed algorithm is compared with the genetic algorithm (GA) and standard particle swarm optimization (SPSO) in terms of parameter accuracy and cost function. To evaluate the identification accuracy, the time-domain output error is designed as the fitness function for parameter optimization. Simulation results show that the proposed method is more successful than other algorithms for parameter identification of fractional order chaotic systems.

A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization

Optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GA-NPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.

A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS

An automated guided vehicle (AGV) is a mobile robot with remarkable industrial applicability for transporting materials within a manufacturing facility or a warehouse. AGV scheduling refers to the process of allocating AGVs to tasks, taking into account the cost and time of operations. Multiobjective scheduling is adopted in this study to acquire a more complex and combinatorial model in contrast with single objective practices. The model objectives are the makespan and number of AGVs minimization while considering the AGVs battery charge. A fuzzy hybrid GA-PSO (genetic algorithm – particle swarm optimization) algorithm was developed to optimize the model. Results have been compared with GA, PSO, and hybrid GA-PSO algorithms to explore the applicability of the algorithm developed. Model’s feasibility and the algorithms’ performance were investigated through a numerical example before and after the optimization. The model evaluation and validation was conducted through simulation via Flexsim software. The fuzzy hybrid GA-PSO surpassed the other methods, although obtaining less mean computational time was the only significant improvement over hybrid GA-PSO.

Hybrid Particle Swarm Optimization-Genetic Algorithm based Optimal dispatch of Wind and Thermal System

This paper discusses a hybrid Particle Swarm Optimization-Genetic Algorithm for optimal dispatch of wind-thermal generation system. In the power system when wind power penetration increases, its impact need to be investigated on conventional thermal unit as wind power is intermittent and unpredictable. Particle Swarm Optimization and Genetic Algorithm are used to coordinate the wind and thermal power system to find the minimum production cost including the valve effect of thermal units. The effectiveness of the proposed approach is validated using three IEEE test systems consisting of 6, 13 and 40 units and a NTPS utility system consisting of 7 generating units with two wind farms of 100MW and 150MW respectively. Simulation results show that the inclusion of wind power generation reduces the total fuel cost.

A novel particle swarm and genetic algorithm hybrid method for diesel engine performance optimization

Heuristic methods have been a successful tool for optimizing engine parameters in both simulation and experimental testing. An improved hybrid method applying both the particle swarm optimization method and genetic algorithm was developed, tested, and compared with a basic particle swarm method for improving engine emissions and performance. A computational comparison between the particle swarm optimization–genetic algorithm hybrid, basic particle swarm optimization, and basic genetic algorithm was done using standard test problems. Computational results indicated improvements in both the efficiency and effectiveness of the present hybrid method. Engine testing was performed under steady-state conditions at 1400 r/min at 4.15 bar brake mean effective pressure. The basic particle swarm optimization and the hybrid particle swarm optimization–genetic algorithm method were applied to the test apparatus and used to locate the optimum neighborhood of the engine operation. A single-objective function representing NOx, particulate matter, hydrocarbon, CO, and fuel consumption was used in this application. The hybrid method was able to locate a narrow window of operation which showed 27% lower NOx emissions and 60% lower particulate matter emissions than the standard particle swarm optimization method. The hybrid method was able to locate the improvements using similar dynamometer time, indicating that the hybrid method is more efficient and more effective. Trends relating combustion characteristics and input parameters were observed and are discussed with regard to future improvements to heuristic methods for optimizing diesel engine performance.

A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA

Efficient optimization algorithms are critical to the development of new engine technology. In this study, experimental investigations were carried out on optimizing the performance of a four-cylinder, turbocharged, direct-injection diesel engine running with soy biodiesel. An effective hybrid particle swarm optimization (PSO) and genetic algorithm (GA) method using a small population was developed and tested to optimize five operating parameters, including EGR rate, pilot timing, pilot ratio, main injection timing, and injection pressure. Based on the measured engine performance and emissions, results show that the new hybrid algorithm can significantly speed up the optimization process and achieve a superior optimum as compared to the basic GA method. The new hybrid PSO–GA method is expected to perform as an effective tool for rapid engine performance optimization.

Spike Detection from Electroencephalogram Signals with Aid of Hybrid Genetic Algorithm-Particle Swarm Optimization

Spike Detection from Electroencephalogram Signals with Aid of Hybrid Genetic Algorithm-Particle Swarm Optimization

Thermal-Aware Non-slicing VLSI Floorplanning Using a Smart Decision-Making PSO-GA Based Hybrid Algorithm

Floorplanning is an important physical design step in the ASIC design flow. It is the process of estimating the area to be occupied by various blocks in a layout together with a precise interconnection pattern. In this work, a smart decision-making hybrid particle swarm optimization-genetic algorithm that aims at reducing the area, wirelength, and hotspot by distributing the temperature evenly across the chip is presented. B*-tree is used to generate the initial floorplan and later a PSO-GA based hybrid algorithm is used to obtain an optimal placement solution. Temperature-driven floorplanning is considered at the perturbation stage to separate the hotspots, thereby reducing the average and maximum temperature. The experimental results of the proposed algorithm are compared with other stochastic algorithms using MCNC and Alpha processor floorplan benchmark circuits. The result shows that the proposed algorithm performs efficient floorplanning, with reduced average and peak temperature.

Two-echelon logistics distribution region partitioning problem based on a hybrid particle swarm optimization–genetic algorithm

Two-echelon logistics distribution region partitioning is a critical step to optimize two or multi-echelon logistics distribution network, and it aims to assign distribution unit to a certain logistics facility (i.e. logistic center and distribution center). Given the partitioned regions, vehicle routing problem can be further developed and solved. This paper established a model to minimize the total cost of the two-echelon logistics distribution network. A hybrid algorithm named as the Extended Particle Swarm Optimization and Genetic Algorithm (EPSO–GA) is proposed to tackle the model formulation. A two-dimensional particle encoding method is adopted to generate the initial population of particles. EPSO–GA combines the merits of Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) with both global and local search capability. By updating the inertia weight and exchanging best-fit solutions and worst-fit solutions between PSO and GA, EPSO–GA algorithm is able to converge to an optimal solution with a reasonable design of termination and iteration rules. The computation results from a case study in Guiyang city, China, reveal that EPSO–GA algorithm is superior to the other three algorithms, Hybrid Particle Swarm Optimization (HPSO), GA, and Ant Colony Optimization (ACO), in terms of the partitioning schemes, the total cost and number of iterations. By comparing with the exact method, the proposed approach demonstrates its capability to optimize a small scale two-echelon logistics distribution network. The proposed approach can be readily implemented in practice to assist the logistics operators reduce operational costs and improve customer service. In addition, the proposed approach is of great potential to apply in other research domains.

Hybrid GA–PSO for optimal placement of static VAR compensators in power system

In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.

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.

A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem

This paper presents a novel hybrid genetic algorithm (GA)-particle swarm optimization (PSO) approach for reliability redundancy allocation problem (RRAP) in series, series–parallel, and complex (bridge) systems. The proposed approach maximizes overall system reliability while minimizing system cost, system weight and volume, simultaneously, under nonlinear constraints. To meet these objectives, an adaptive hybrid GA–PSO approach is developed to identify the optimal solutions and improve computation efficiency for these NP-hard problems. An illustrative example is applied to show the capability and effectiveness of the proposed approach. According to the results, in all three cases, reliability values are improved. Moreover, computational time and variance are decreased compared to the similar studies. The proposed approach could be helpful for engineers and managers to better understand their system reliability and performance, and also to reach a better configuration.

A MODEL OF HYBRID GENETIC ALGORITHM-PARTICLE SWARM OPTIMIZATION(HGAPSO) BASED QUERY OPTIMIZATION FOR WEB INFORMATION RETRIEVAL

The rapid growth of web pages available on the Internet recently, searching relevant and up-to-date information has become a crucial issue. Information retrieval is one of the most crucial components in search engines and their optimization would have a great effect on improving the searching efficiency due to dynamic nature of web it becomes harder to find relevant and recent information. That’s why more and more people begin to use focused crawler to get information in their special fields today. Conventional search engines use heuristics to determine which web pages are the best match for a given keyword. Earlier results are obtained from a database that is located at their local server to provide fast searching. However, to search for the relevant and related information needed is still difficult and tedious. This paper presents a model of hybrid Genetic Algorithm -Particle Swarm Optimization (HGAPSO) for Web Information Retrieval. Here HGAPSO expands the keywords to produce the new keywords that are related to the user search.

Web Information Retrieval Using Genetic Algorithm-Particle Swarm Optimization

The rapid growth of web pages available on the Internet recently, searching relevant and up-to-date information has become a crucial issue. Information retrieval is one of the most crucial components in search engines and their optimization would have a great effect on improving the searching efficiency due to dynamic nature of web it becomes harder to find relevant and recent information. That's why more and more people begin to use focused crawler to get information in their special fields today. Conventional search engines use heuristics to determine which web pages are the best match for a given keyword. Earlier results are obtained from a database that is located at their local server to provide fast searching. However, to search for the relevant and related information needed is still difficult and tedious. This paper presents a model of hybrid Genetic Algorithm -Particle Swarm Optimization (HGAPSO) for Web Information Retrieval. Here HGAPSO expands the keywords to produce the new keywords that are related to the user search.

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.

A Hybrid GA-PSO Approach Based on Similarity for Various Types of Economic Dispatch Problems

Economic dispatch problem is an optimization problem where objective function is highly nonlinear. In this paper, an efficient method based on hybrid genetic algorithm- particle swarm optimization (GA-PSO) for economic dispatch (ED) problem is proposed. In the proposed method, children created by using similarity measurement between mother and father chromosomes relationship. The feasibility of the proposed approach is demonstrated for solve various types of economic dispatch (ED) problems in power systems such as, economic dispatch with valve point (EDVP) effects, the ED of generators with prohibited operating zones and ED with only fuel options and it is compared in the recent literature. The study results show that the proposed approach is more efficient in finding higher quality solutions in various type ED problems. Ill. 3, bibl. 20, tabl. 10 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.155