Particle Swarm Genetic Algorithm Research Articles

Application of the hybrid genetic particle swarm algorithm to design the linear quadratic regulator controller for the accelerator power supply

The purpose of this paper is to study a new method to improve the performance of the magnet power supply in the experimental ring of HIRFL-CSR. A hybrid genetic particle swarm optimization algorithm is introduced, and the algorithm is applied to the optimal design of the LQR controller of pulse width modulated power supply. The fitness function of hybrid genetic particle swarm optimization is a multi-objective function, which combined the current and voltage, so that the dynamic performance of the closed-loop system can be better. The hybrid genetic particle swarm algorithm is applied to determine LQR controlling matrices Q and R. The simulation results show that adoption of this method leads to good transient responses, and the computational time is shorter than in the traditional trial and error methods. The results presented in this paper show that the proposed method is robust, efficient and feasible, and the dynamic and static performance of the accelerator PWM power supply has been considerably improved.

Vehicle State Estimation Based on Unscented Kalman Filtering and a Genetic-particle Swarm Algorithm

Vehicle state and parameter estimation has gradually become an important way to soft-sense some variables that are difficult to measure directly using general sensors. In the traditional Kalman filtering algorithm, the selection of the process noise covariance matrix and the measurement noise covariance matrix will directly affect the filtering accuracy of the algorithm. In order to improve the filtering accuracy of the filter algorithm to obtain the optimal solution, based on a 7-DOF nonlinear vehicle dynamics model and the Magic formula tire model, a hybrid algorithm containing an unscented Kalman filter (UKF) and a genetic-particle swarm algorithm (genetic-particle swarm UKF) is used to estimate several vehicle key states. Compared with traditional estimator based on UKF and the unscented particle filter (UPF), the simulation and real vehicle test results show that the proposed estimator based on the genetic-particle swarm UKF algorithm has higher accuracy and less computation requirements than the UKF estimator. And also, the proposed hybrid algorithm has superiority on the convergence speed of the optimization than the UPF algorithm. The results of a real-vehicle experiment demonstrate that the proposed hybrid algorithm can be used effectively for solving the vehicle-state estimation problem.

Economic optimization of heat exchanger networks based on geometric parameters using hybrid genetic-particle swarm algorithm technique

PurposeThe purpose of this study is to provide a method for designing the shell and tube heat exchangers and examine the total annual cost of heat exchanger networks from the economic view based on the careful design of equipment.Design/methodology/approachAccurate evaluation of heat exchanger networks performance depends on detailed models of heat exchangers design. The simulations variables include nine design variables such as flow direction determination of each of the two fluids, number of tubes, number of tube passes, length of tubes, the arrangement of tubes, size and percentage of baffle cut, tube diameter and tube pitch. The optimal designing of the heat exchangers is based on geometrical and hydraulic modeling and using a hybrid genetic particle swarm optimization algorithm (PSO-GA) technique. In this paper, optimization and minimization of the total annual cost of heat exchanger networks are considered as the objective function.FindingsIn this study, a fast and reliable method is used to simulate, optimize design parameters and evaluate heat transfer enhancement. PSO-GA algorithms have been used to minimize the total annual cost, which includes investment costs of heat exchangers and pumps, operating costs (pumping) and energy costs for utilities. Three case studies of four, six and nine streams are selected to demonstrate the accuracy of the method. Reductions of 0.55%, 23.5% and 14.78% are obtained in total annual cost for the selected streams, respectively.Originality/valueIn the present study, a reliable method is used to simulate and optimize design parameters and the economic optimization of the heat exchanger networks. Taking into account the importance of shell and tube heat exchangers in industrial applications and the complexity in their geometry, the PSO-GA methodology is adopted to obtain an optimal geometric configuration. The total annual cost is chosen as the objective function. Applying this technique to case studies demonstrates its ability to accurately design heat exchangers to optimize the objective function of the heat exchanger networks by giving the detail of design.

Optimal Bus Layout in Transmission System by Using Meta-heuristic Approaches

Transmission system expansion leads to excessive short-circuit currents that exceed the capacity of circuit breakers. To avoid over short-circuit current; transmission system operators make various alterations in the transmission system by changing the topology of the system. The feeders can be distributed to different buses in substations through disconnecting coupling circuit breaker between buses. Having been interchanged, the transmission system may face up a lot of problems even in single outage. Therefore, the optimal positions of feeders in substations are important in order to maintain system security. However, this optimization problem has non-convex properties due to AC load flow equations and it has a multi-objective structure to provide the limitation of short-circuit current and security of N-1 contingency. The constraints are the short-circuit current, voltage and transmission line limits in the single contingency. In this paper, the Genetic Algorithm and Binary Particle Swarm Optimization methods were utilized to find a near-optimal bus layout. Algorithms coding was made with Python programming language and PSS/E program was used to obtain power flow and short-circuit data. The results of applying the methods to the IEEE 14-bus test system demonstrated the effectiveness of the methods to take overloads away and restrict short-circuit current and hold voltage in its limit.

Integration of fuzzy logic with Metaheuristics for education center site selection

Education is one of the most vital sectors of any nation’s development. Site selection for Education Centers (EC) like schools, colleges, and coaching centers can be a very complex process. Various parameters like population, literacy rate, property cost, etc. have to be considered while selecting a site. Though deterministic approaches employed for site selection have been proven to give the best possible solution, they fail to work on large datasets. Recently metaheuristics have become very popular for solving optimization problems. This paper presents two integrated approaches, Fuzzy Genetic Algorithm for EC site selection (FGA-ECSS) and Fuzzy Binary Particle Swarm Optimization for EC site selection (FBPSO-ECSS) for choosing sites optimally. To evaluate the effectiveness of the two approaches, FGA-ECSS and FBPSO-ECSS have been compared with each other as well as with Genetic Algorithm and Binary Particle Swarm Optimization. The results obtained from the proposed solutions are promising and indicate that they can be used for solving such optimization problems.

Research on Green Logistics Scheduling System of Textile Vehicle Automatic Guided Vehicle

With the rapid development of science and technology, China’s modern manufacturing technology has developed and progressed rapidly, and the degree of automation of intelligent production workshops has gradually increased. Enterprises are facing huge challenges in path optimization and energy conservation and emission reduction. This paper mainly studies the problem of AGV green intelligent logistics scheduling in textile workshops. First, an AGV logistics scheduling optimization model with the goal of reducing AGV energy consumption and optimal AGV path is established, and then an improved genetic particle swarm optimization algorithm with task sequencing as the constraint is proposed. Take the actual scheduling data of a textile workshop as an example to verify the method in this paper. From the calculation results, the AGV logistics scheduling model proposed in this paper can better simulate the energy consumption problem of AGV green scheduling. The improved genetic particle swarm algorithm proposed has a better optimization ability and a faster speed in solving such problems. Speed of convergence.

Optimization method of vehicle handling stability based on response surface model with D-optimal test design

In order to improve the handling stability of a vehicle, an optimization method of vehicle handling stability based on D-optimal test design was proposed in this paper. The multibody dynamic model was established and verified by experiments. On this basis, a response surface model was established based on D-optimal test design. An improved genetic-particle swarm algorithm was used to optimize the vehicle handling stability. The general evaluation score of vehicle handling stability was taken as the optimization objective. The vehicle structural parameters, tire and spring characteristics were regarded as design variables. The results showed that the multi-body dynamic model was accurate. After optimization, the general evaluation score of vehicle handling stability increased by 8.98 %; the score of the steering returnability and steady static circular test was increased by 20.43 % and 27.31 %, respectively. Then from the sensitivity of the optimization variables to the stability of the vehicle's handling, the rear wheel lateral stiffness has the greatest impact on it, with a sensitivity of 86.9 %; the wheelbase has the smallest impact on it, with a sensitivity of -3.39 %, which can be reduced in future optimization variable to improve design efficiency.

Partial Disassembly Line Balancing Problem Analysis Based on Sequence-Dependent Stochastic Mixed-Flow

Abstract Given the characteristics of decommissioning product diversity, wear difference, and structural complexity, we analyzed the disassembly line balance problems of minimum disassembly workstation, station load balancing, priority disassembly of high-demand parts and the lowest cost of invalid operations, and further considered the influence of random factors on actual disassembly operations. The purpose of this paper is to establish a model of sequence-dependent stochastic mixed-flowed partial disassembly line balancing problem and propose an adaptive hybrid particle swarm genetic algorithm to solve the model. The algorithm replaces the fixed value genetic operator with an adaptive crossover and mutation operator to improve the global optimization ability. The introduction of adaptive weighted particles leads to improved local optimization ability of the algorithm so that the algorithm has strong global and regional optimization ability to improve algorithm accuracy. The effectiveness of the proposed algorithm is verified by solving the classic disassembly line balancing problem with different scales and comparing it with the solution results of underlying genetic and particle swarm optimization algorithm. Meanwhile, the proposed model and algorithm are applied to the mixed-flow disassembly engineering project of a 25-task reducer. The results indicate that the proposed model is superior to the control group in terms of minimal disassembly workstation, station load balancing, invalid operation cost, and overall performance of the disassembly line, which verifies the validity of the model.

Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm

With the development of network technology and the continuous advancement of society, the combination of various industries and the Internet has produced many large-scale complex networks. A common feature of complex networks is the community structure, which divides the network into clusters with tight internal connections and loose external connections. The community structure reveals the important structure and topological characteristics of the network. The detection of the community structure plays an important role in social network analysis and information recommendation. Therefore, based on the relevant theory of complex networks, this paper introduces several common community detection algorithms, analyzes the principles of particle swarm optimization (PSO) and genetic algorithm and proposes a particle swarm-genetic algorithm based on the hybrid algorithm strategy. According to the test function, the single and the proposed algorithm are tested, respectively. The results show that the algorithm can maintain the good local search performance of the particle swarm optimization algorithm and also utilizes the good global search ability of the genetic algorithm (GA) and has good algorithm performance. Experiments on each community detection algorithm on real network and artificially generated network data sets show that the particle swarm-genetic algorithm has better efficiency in large-scale complex real networks or artificially generated networks.

A new approach for determining multi-objective optimal control of semilinear parabolic problems

In this paper, two approaches based on evolutionary algorithms are applied to solve a multi-objective optimal control problem governed by semilinear parabolic partial differential equations. In this approach, first, we change the problem into a measure-theoretical one, replace this with an equivalent infinite dimensional multi-objective nonlinear programming problem and apply approximating schemes. Finally, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization are employed to obtain Pareto optimal solutions of the problem. Numerical examples are presented to show the efficiency of the given approach.

Mountain railway alignment optimization using stepwise & hybrid particle swarm optimization incorporating genetic operators

Optimizing railway alignments is a quite complex and time-consuming engineering problem. The huge continuous search space, complex constraints, implicit objective function and infinite potential alternatives of this problem pose many challenges. Especially in mountainous regions, finding a near-optimal alignment for extremely complex terrain and constraints is a most arduous task, which cannot be solved satisfactorily with most existing methods. In this study, a stepwise & hybrid particle swarm-genetic algorithm is developed for railway alignment optimization in mountainous regions. It is a continuous search method suitable for railway alignment design. A stepwise horizontal–vertical–integral approach which defines the horizontal and vertical alignments as two kinds of particles, is proposed to solve the three-dimensional railway alignment optimization problem. To enhance the initial diversity and momentum, butterfly-shaped areas are preset on a path generated with a bidirectional distance transform for initializing horizontal particles. For the solution method, specific genetic operators, including roulette wheel selection, four crossovers and two mutations are integrated into the stepwise particle swarm method to address parameter-dependent performance and avoid premature convergence. In addition, a cubic polynomial weight update strategy is employed for thoroughly searching the problem space. This synthesis method has been applied to a real-world case in a very mountainous region. The detailed data analyses demonstrate that it can offer more promising solutions compared with alternatives designed by experienced designers and those generated with a genetic algorithm or non-stepwise particle swarm algorithm.

Multi-Objective Optimization and Analysis of Tri-Stable Permanent Magnet Actuators for Valve Application

Tri-stable permanent magnet actuator (TPMA) has been widely used in industry. In this study, a new TPMA integrating a permanent magnet actuator and two centralizing disc springs is proposed for valve application. By taking the linear coefficient of force/stroke characteristic, startup force, holding force and response time as the optimization objective, the TPMA was optimized based on an improved hierarchical genetic-particle swarm algorithm (HGP) and 3D Finite element analysis (FEA) under volume constrain. The optimization process was analyzed in detail to prove the effectiveness of multi-objective design. The performance of the prototype was verified experimentally. The results showed that a good linear force/stroke characteristic of prototype was obtained, and the linear coefficient of force/stroke characteristic was 0.97. The startup force was 409 N and holding force was 332 N, which increased by 32% and 54% after optimization, respectively. The dynamic response time of prototype from center stability to extreme stability was 5.8 ms. The TPMA can be applied extensively in the valve control.

MAI Mitigation in MC-CDMA Systems Using Social Impact Based Wireless Communication Algorithm

In this paper a novel optimization technique i.e. Social Impact based Wireless Communication Algorithm (SIWCA) has been applied on multi-carrier code division multiple access (MC-CDMA) communication systems to mitigate multiple access interference (MAI). MC-CDMA is being researched as an alternate technology for fourth generation (4G) as well as fifth generation (5G) mobile systems. MAI has been a major concern for the CDMA based systems. MAI increases the bit error rate in a MC-CDMA system, which in turn degrades the system performance. The SIWCA is based on the social impact theory of human behavior in the society. The proposed approach combines the social sciences with the communication technology. The simulation results show that SIWCA based MC-CDMA detector is capable of significantly reducing the MAI and gives a near-optimum performance. Further SIWCA is compared with popular optimization techniques like genetic algorithm (GA) and binary particle swarm optimization (PSO) for different parameters. Simulation results show that SIWCA converges fast and works with lesser number of control parameters as compared to GA and PSO.

Dynamic cellular manufacturing system considering machine failure and workload balance

Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators’ assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in small-sized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.

Design of Linear Phase High Pass FIR Filter using Weight Improved Particle Swarm Optimization

The design of Finite Impulse Response (FIR) digital filter involves multi-parameter optimization, while the traditional gradient-based methods are not effective enough for precise design. The aim of this paper is to present a method of designing 24th order high pass FIR filter using an evolutionary heuristic search technique called Weight Improved Particle Swarm Optimization (WIPSO). A new function of the weight parameters is constructed for obtaining a better optimal solution with faster computation. The performance of the proposed algorithm is compared with two other search optimization algorithms namely standard Genetic Algorithm (GA) and conventional Particle Swarm Optimization (PSO). The simulation results show that the proposed WIPSO algorithm is better than GA and PSO in terms of the magnitude response accuracy and the convergence speed for the design of 24th order high pass FIR filter.

New mathematical modeling for a location\u2013routing\u2013inventory problem in a multi-period closed-loop supply chain in a car industry

This paper studies a location–routing–inventory problem in a multi-period closed-loop supply chain with multiple suppliers, producers, distribution centers, customers, collection centers, recovery, and recycling centers. In this supply chain, centers are multiple levels, a price increase factor is considered for operational costs at centers, inventory and shortage (including lost sales and backlog) are allowed at production centers, arrival time of vehicles of each plant to its dedicated distribution centers and also departure from them are considered, in such a way that the sum of system costs and the sum of maximum time at each level should be minimized. The aforementioned problem is formulated in the form of a bi-objective nonlinear integer programming model. Due to the NP-hard nature of the problem, two meta-heuristics, namely, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO), are used in large sizes. In addition, a Taguchi method is used to set the parameters of these algorithms to enhance their performance. To evaluate the efficiency of the proposed algorithms, the results for small-sized problems are compared with the results of the ε-constraint method. Finally, four measuring metrics, namely, the number of Pareto solutions, mean ideal distance, spacing metric, and quality metric, are used to compare NSGA-II and MOPSO.

A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar

A hybrid optimization approach combining a particle swarm algorithm, a genetic algorithm, and a heuristic inter-leaving algorithm is proposed for scheduling tasks in the multifunction phased array radar. By optimizing parameters using chaos theory, designing the dynamic inertia weight for the particle swarm algorithm as well as introducing crossover operation and mutation operation of the genetic algorithm, both the efficiency and exploration ability of the hybrid algorithm are improved. Under the frame of the intelligence algorithm, the heuristic interleaving scheduling algorithm is presented to further use the time resource of the task waiting duration. A large-scale simulation demonstrates that the proposed algorithm is more robust and efficient than existing algorithms.

Hierarchical genetic-particle swarm optimization for bistable permanent magnet actuators

Bistable permanent magnet actuator (BPMA) has been widely used in industry as a kind of energy converter. However, its power consumption and response performance are in conflict during operating because of the conflict between holding force and initial force (the minimum threshold driving force). An optimization procedure based on a novel hierarchical genetic-particle swarm algorithm (HGP) for a hybrid excited linear actuator (HELA) constrained in a specific volume has been developed. For obtaining the objective function for each parameter combination precisely, three-dimensional finite element analysis (FEA) was employed. Meanwhile, the improvement of optimization efficiency and quality is extremely demanding because the FEA needs much computing time. The proposed hybrid algorithm adopts a hierarchical structure from the concept of “experimental classes” in China. The base level is composed of the majority general individuals of Genetic algorithms (GA), which provides the global search ability of the entire algorithm. The top level comprises the minority elite consisting of the better individual, which performs the accurate local search by using the particle swarm optimization (PSO) algorithm with variable inertia weight and constriction coefficient. Additionally, the performance of HGP has been evaluated through the Rosenbrock function, and the proposed method is superior to other related methods Finally, the proposed procedure was verified by optimizing HELA's parameters in multidimensional parameters space under the design constraint. The results show that both of the holding force and the initial force are improved more than 25% compared with the initial design, which ensures both low power consumption and fast response.

Multiobjective Optimization of Multistage Synchronous Induction Coilgun Based on NSGA-II

The structure and trigger control strategy have become the most important factors that restrict the performance of the multistage synchronous induction coilgun (MSSICG). However, it is still a difficult task to design MSSICG under overload constraint due to coupling between the multiple parameters. In this paper, the maximization of the emission efficiency and acceleration stationarity is treated as a multiobjective optimization problem. By analyzing the relationship between the number of turns and the other structural parameters of the launch, the multiobjective optimization model of MSSICG is established by the current filament method which was verified by the experimental data and finite-element method. And then the second generation nondominated sorting genetic algorithm (NSGA-II) and multiobjective particle swarm optimization (MOPSO) were employed to optimize the model in order to maximize the energy transfer efficiency while achieving the smooth acceleration of the armature. With the formulated optimization model, a five-stage synchronous induction coilgun is optimized as a special case. A decision-making procedure based on the fuzzy membership function is used for obtaining best compromise solution from the set of Pareto-solutions obtained through NSGA-II and MOPSO. In addition, the optimization performance of the proposed multiobjective optimization model and the single-objective optimization model of the MSSICG was compared. The result of optimization shows that the proposed multiobjective optimization model of MSSICG can effectively improve the performance of the coilgun compared with the single-objective optimization model which takes of the launch velocity and overload acceleration as the combination objective function or only the launch velocity.

Optimization of a nonlinear model for predicting the ground vibration using the combinational particle swarm optimization-genetic algorithm

When particle's wave velocity resulting from mining blasts exceeds a certain level, then the intensity of produced vibrations incur damages to the structures around the blasting regions. Development of mathematical models for predicting the peak particle velocity (PPV) based on the properties of the wave emission environment is an appropriate method for better designing of blasting parameters, since the probability of incurred damages can considerably be mitigated by controlling the intensity of vibrations at the building sites. In this research, first out of 11 blasting and geo-mechanical parameters of rock masses, four parameters which had the greatest influence on the vibrational wave velocities were specified using regression analysis. Thereafter, some models were developed for predicting the PPV by nonlinear regression analysis (NLRA) and artificial neural network (ANN) with correlation coefficients of 0.854 and 0.662, respectively. Afterward, the coefficients associated with the parameters in the NLRA model were optimized using optimization particle swarm-genetic algorithm. The values of PPV were estimated for 18 testing dataset in order to evaluate the accuracy of the prediction and performance of the developed models. By calculating statistical indices for the test recorded maps, it was found that the optimized model can predict the PPV with a lower error than the other two models. Furthermore, considering the correlation coefficient (0.75) between the values of the PPV measured and predicted by the optimized nonlinear model, it was found that this model possesses a more desirable performance for predicting the PPV than the other two models.