Hybrid Genetic Particle Swarm Optimization Research Articles

Study of mixed materials heat exchanger using optimization techniques

PurposeThis paper aims to examine total annual cost from economic view mixed materials heat exchangers based on three optimization algorithms. This study compares the use of three optimization algorithms in the design of economic optimization shell and tube mixed material heat exchangers.Design/methodology/approachA shell and tube mixed materials heat exchanger optimization design approach is expanded based on the total annual cost measured by dividing the costs of the heat exchanger to area of surface and power consumption. In this study, optimization and minimization of the total annual cost is considered as the objective function. There are three types of exchangers: cheap, expensive and mixed. Mixed materials are used in corrosive flows in the heat exchanger network. The present study explores the use of three optimization techniques, namely, hybrid genetic-particle swarm optimization, shuffled frog leaping algorithm techniques and ant colony optimization.FindingsThere are three parameters as decision variables such as tube outer diameter, shell diameter and central baffle spacing considered for optimization. Results have been compared with the findings of previous studies to demonstrate the accuracy of algorithms.Originality/valueThe present study explores the use of three optimization techniques, namely, hybrid genetic-particle swarm optimization, shuffled frog leaping algorithm techniques and ant colony optimization. This study has demonstrated successful application of each technique for the optimal design of a mixed material shell and tube heat exchanger from the economic view point.

Hybrid Support Vector Regression with Parallel Co-Evolution Algorithm Based on GA and PSO for Forecasting Monthly Rainfall

Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regression (SVR) is a very useful precipitation prediction model. In this paper, a novel parallel co-evolution algorithm is presented to determine the appropriate parameters of the SVR in rainfall prediction based on parallel co-evolution by hybrid Genetic Algorithm and Particle Swarm Optimization algorithm, namely SVRGAPSO, for monthly rainfall prediction. The framework of the parallel co-evolutionary algorithm is to iterate two GA and PSO populations simultaneously, which is a mechanism for information exchange between GA and PSO populations to overcome premature local optimum. Our methodology adopts a hybrid PSO and GA for the optimal parameters of SVR by parallel co-evolving. The proposed technique is applied over rainfall forecasting to test its generalization capability as well as to make comparative evaluations with the several competing techniques, such as the other alternative methods, namely SVRPSO (SVR with PSO), SVRGA (SVR with GA), and SVR model. The empirical results indicate that the SVRGAPSO results have a superior generalization capability with the lowest prediction error values in rainfall forecasting. The SVRGAPSO can significantly improve the rainfall forecasting accuracy. Therefore, the SVRGAPSO model is a promising alternative for rainfall forecasting.

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.

A hybrid GA-PSO optimization algorithm for conformal antenna array pattern synthesis

This paper introduces a hybrid genetic algorithm and particle swarm optimization algorithm (GA-PSO) dedicated to pattern synthesis of conformal antenna arrays. Taking advantages of both methods, the proposed hybrid GA-PSO optimization algorithm has fast convergence speed and high convergence accuracy when applied to antenna array pattern synthesis. To show the performance of the hybrid optimization algorithm, several typical test functions and optimization examples of a linear array pattern synthesis are illustrated. Finally, a 4 × 2 cylindrical conformal microstrip antenna array as a practical synthesis example is studied to demonstrate the proposed algorithm. The simulated and measured results have shown the proposed method is effective and reliable for conformal antenna array pattern synthesis.

Task scheduling in a cloud computing environment using HGPSO algorithm

Cloud computing delivers computing resources like software and hardware as a service to the users through a network. The main idea of cloud computing is to share the tremendous power of storage, computation and information to the scientific applications. In cloud computing, the user tasks are organized and executed with suitable resources to deliver the services effectively. There are plenty of task allocation techniques that are used to accomplish task scheduling. In order to enhance the task scheduling technique, an efficient task scheduling algorithm is proposed in this paper. Optimization techniques are very popular in solving NP-hard problems. In this proposed technique, user tasks are stored in the queue manager. The priority is calculated and suitable resources are allocated for the task if it is a repeated task. New tasks are analyzed and stored in the on-demand queue. The output of the on-demand queue is given to the Hybrid Genetic-Particle Swarm Optimization (HGPSO) algorithm. To implement HGPSO technique, genetic algorithm and particle swarm optimization algorithm are combined and used. HGPSO algorithm evaluates suitable resources for the user tasks which are in the on-demand queue.

Bi-level optimisation model for greener transportation with intelligent transport system

In this paper, we propose a bi-level optimisation model (BLOM) with three algorithms. BLOM is intended for fuel saving and carbon dioxide emission reduction in both upper-level and lower-level model with intelligent transport system. Traffic signal schemes are optimised for minimising total fuel consumption passing through a road intersection in unit time in the upper-level model. At the same time, traffic signal information data are sent to the lowerlevel model in which vehicle motion states are optimised for greener transportation. Three algorithms include hybrid genetic algorithm and particle swarm optimisation in upper-level model with hybrid genetic algorithm and particle swarm optimisation in lower-level model (GA-PSO/GA-PSO), GA in upper-level model with PSO in lower-level model (GA/PSO) and GA in both level model (GA/GA) are realised to compare and improve the performance of the model. The simulation results derive GA-PSO/GA-PSO hybrid algorithm converges faster with the best resolution and least calculation time than other GA/PSO and GA/GA algorithms.

Transversely polarized sub-diffraction optical needle with ultra-long depth of focus

We generated purely transversely polarized sub-diffraction optical needles with ultra-long depth of focus (DOF) by focusing azimuthally polarized (AP) beams that were modulated by a vortex 0–2π phase plate and binary phase diffraction optical elements (DOEs). The concentric belts’ radii of the DOEs were optimized by a hybrid genetic particle swarm optimization (HGPSO) algorithm. For the focusing system with the numerical aperture (NA) of 0.95, an optical needle with the full width at half maximum (FWHM) of 0.40λ and the DOF of 6.23λ was generated. Similar optical needles were also generated by binary phase DOEs with different belts. The results demonstrated that the binary phase DOEs could achieve smaller FWHMs and longer DOFs simultaneously. The generated needles were circularly polarized on the z-axis and there were no longitudinally polarized components in the focal fields. The radius fabrication errors of a DOE have little effect on the optical needle produced by itself. The generated optical needles can be applied to the fields of photolithography, high-density optical data storage, microscope imaging and particle trapping.

A multi-balanced hybrid optimization technique to track objects using rough set theory

In this paper, the process of object detection and tracking is performed by means of five stages, namely frame segregation, shot segmentation, shape and texture feature extraction, object detection in frames through rough set theory and soft computing evolutionary programming with hybrid genetic algorithm particle swarm optimization. In the first stage, the input video file is segregated into number of frames and then the image frame from the specific shots is alone separated in the second stage with the help of DCT transformations. The third phase involves extracting shape and texture features from the shot segmented image frames.

New hybrid genetic particle swarm optimization algorithm to design multi-zone binary filter.

The binary phase filters have been used to achieve an optical needle with small lateral size. Designing a binary phase filter is still a scientific challenge in such fields. In this paper, a hybrid genetic particle swarm optimization (HGPSO) algorithm is proposed to design the binary phase filter. The HGPSO algorithm includes self-adaptive parameters, recombination and mutation operations that originated from the genetic algorithm. Based on the benchmark test, the HGPSO algorithm has achieved global optimization and fast convergence. In an easy-to-perform optimizing procedure, the iteration number of HGPSO is decreased to about a quarter of the original particle swarm optimization process. A multi-zone binary phase filter is designed by using the HGPSO. The long depth of focus and high resolution are achieved simultaneously, where the depth of focus and focal spot transverse size are 6.05λ and 0.41λ, respectively. Therefore, the proposed HGPSO can be applied to the optimization of filter with multiple parameters.

Online Control Threshold Optimization for Complex Mechanical Products Assembly Process Based on Hybrid Genetic Particle Swarm Optimization

Online Control Threshold Optimization for Complex Mechanical Products Assembly Process Based on Hybrid Genetic Particle Swarm Optimization

A Reliable Wide-Area Measurement System Using Hybrid Genetic Particle Swarm Optimization (HGPSO)

The state of the power system has to be estimated continuously and accurately by means of operating parameters i.e. voltage and current phasors. Phasor measurement unit (PMU) is becoming a most prominent tool for monitoring, control and protection of electric networks, and hence it is required to employ them for the present and future power system networks. Hence an Optimal PMU Placement (OPP) is quite important during the planning studies for both existing and future power networks. So, when a new state estimator is commissioned, or an existing estimator is up-graded, the problems of minimizing the number of PMUs and their optimal location for system with complete observability will come into scenario. This paper presents a novel Hybrid Genetic Particle Swarm Optimization (HGPSO) method based approach for a power system to have complete observability based optimal PMU placement problem subjected to all possible contingencies and PMU communication channel limitations. The proposed method is tested with some of standard IEEE test systems and also practiced on some Inter Regional Power Grids (IRPGs) of the Indian power system using MATLAB. The results obtained were also compared with existing techniques that have been already applied on the test systems said above, were proved to be the best and effective.

Flowshop with Multiple Processors Dynamic Scheduling Problem based on Hybrid Genetic-Particle Swarm Optimization

Flowshop with Multiple Processors Dynamic Scheduling Problem based on Hybrid Genetic-Particle Swarm Optimization

Mining association rules using hybrid genetic algorithm and particle swarm optimisation algorithm

Evolutionary computation has become the popular choice for solving complex problems, which are otherwise difficult to solve by traditional methods. Genetic algorithm GA and particle swarm optimisation PSO are both population-based heuristic search methods, which are well suited for mining association rules. GA and PSO both have their unique features and limitations. A hybrid method combining both genetic algorithm and particle swarm optimisation called hybrid GA/PSO GPSO is proposed in this paper. This method is used to bring out the balance between exploration and exploitation, which will result in accurate prediction of the mined association rules and consistency in performance. GA reduces the exploitation tasks and exploration is taken care by PSO. The GPSO methodology for mining association rules performs better than the individual performance of both GA and PSO in terms of predictive accuracy and consistency when tested on five benchmark datasets in the University of California Irvine UCI.

Using Intelligent Algorithms to Find the Optimal Placement of Distributed Generation

This study used Hybrid Genetic-Particle Swarm Optimization (HGPSO) and Particle Swarm Optimization (PSO) for the allocation of Distributed Generation (DG) in order to minimize the total real loss and improve voltage profile in a primary distribution system. The mutation operation of the Genetic Algorithm (GA) is implemented into the Particle Swarm Optimization (PSO) approach. One aspect missing from existing approaches is the capability to efficiently site and size a predefined number of DGs. Here, Hybrid Genetic-Particle Swarm Optimization aims to overcome this shortcoming. The results obtained from the proposed algorithm applied to a 45-bus radial distribution system demonstrate its good performance and capability. Results show that the HGPSO is better than PSO in order to obtain the maximum loss reduction as well as maximum voltage profile improvement for each case of optically placed multi-DGs.

Reliability improvement of power system observability with minimum phasor measurement units

The size and complexity of power network and the cost of monitoring equipments, make it unfeasible to monitor the whole system variables. Conventional system analysers use voltages and currents of the network for monitoring purposes, which affects the system analysis, control and protection. A strategic placement of phasor measurement units (PMUs) is crucial to monitor the whole system with minimum number of devices. This study improves a topological circuit observation method to find essential PMUs. Besides the observability of the normal network, observability of abnormal network is considered. Consequently, a high level of system reliability is achieved. The reliability is maintained by observability under bad current data (CT errors) and all possible single single-line outage. These limitations are taken into the account in a hybrid genetic particle swarm optimisation strategy to minimise monitoring cost and avoiding unobservability under abnormal conditions. Proposed algorithm is tested on 14, 30, 39 and 118-bus IEEE standard test systems and a 24-bus network of Mazandaran Regional Electric Company located in north of Iran.

Optimal Distributed Generation Allocation in Distribution Systems Employing Hybrid Genetic-Particle Swarm Optimization to Reduce Losses

Optimal Distributed Generation Allocation in Distribution Systems Employing Hybrid Genetic-Particle Swarm Optimization to Reduce Losses

Multiobjective Design Optimization of Coupled PM Synchronous Motor-Drive Using Physics-Based Modeling Approach

This paper deals with an optimal design of surface-mounted permanent magnet motor geometry for achieving minimum torque ripple, minimum RMS value of phase current, and minimum total harmonic distortion of phase currents, simultaneously. A classic multiobjective function is formed as a combination of these single objectives. A dynamic physics-based phase variable modeling approach is used to indirectly couple the motor geometry in the finite element domain to the drive circuit in a simulink environment. The physical behavior of motor is calculated by nonlinear transient FE analysis with motion. A fast hybrid genetic-particle swarm optimization process is developed for shape optimization of the motor. The results before and after optimization show the expected performance improvements while reducing magnet material and copper size.