Co-evolutionary Particle Swarm Optimization Algorithm Research Articles

A multi-strategy co-evolutionary particle swarm optimization algorithm with its convergence analysis

A multi-strategy co-evolutionary particle swarm optimization algorithm with its convergence analysis

Study of ideal and optimum cascades using Co-evolutionary Particle Swarm Optimization algorithm

Study of ideal and optimum cascades using Co-evolutionary Particle Swarm Optimization algorithm

Parameter identification method for the variable order fractional-order equivalent model of lithium-ion battery

The equivalent model structure, fractional order-value, and basic circuit parameters in a fractional-order equivalent circuit model (FECM) of a battery jointly determine the description ability of the FECM. For a certain model structure, the fractional order-value determines the basic form of frequency domain impedance characteristics of the model. However, in practical applications, the determination of the fractional order-value lacks clear guidelines. In this paper, the orders of FECM are taken as parameters to be identified, and the coevolutionary particle swarm optimization algorithm is used to synchronously identify the parameters and order-values of equivalent model from a complete set of load profile test data. Under the same algorithm conditions, the state description capabilities of FECM are compared based on different fractional order-values. Furthermore, the reasonable fractional order-value interval is determined, which provides a reference value for the order-value selection in the fixed-order FECM. Test results show that for the two fractional order-values used in the 2-FRC equivalent model, when one is a fixed value, the other fluctuates in a moderate range in most cases. According to this principle, a fixed-order FECM is used to identify the battery parameters. On the basis of not losing the description accuracy of FECM, it reduces the computational complexity caused by fractional order operation.

Robust prediction and optimization of gasoline quality using data-driven adaptive modeling for a light naphtha isomerization reactor

• A data-driven adaptive modeling was made for a light naphtha isomerization reactor. • A new integrated machine learning model (i.e., DLS-SVR) was proposed. • DLS-SVR model shows a better predictive peformance than RSM, SVR, and DLWELM models. • RON, RVP, and benzene content of the isomerate gasoline can be robustly predicted. • Gasoline quality was optimized using a combination of DLS-SVR and CPSO algorithm. In this research, a data-driven adaptive model is developed to predict the variables indicating gasoline quality in the light naphtha isomerization process and determine the optimal conditions leading to improved gasoline quality. To this end, an integrated method based on double-level similarity criterion and support vector regression (DLS-SVR) is proposed. The variables that indicate gasoline quality are research octane number (RON), benzene volume percentage (BVP), and Reid vapor pressure (RVP). In addition to the influential operating variables of pressure, temperature, feed weight hourly space velocity (WHSV), and hydrogen to naphtha feed molar ratio, the model considers benzene’s feed concentration and cycloparaffin content. Experiments are conducted using commercial Pt/Al 2 O 3 -CCl 4 catalyst in a pilot-scale packed-bed reactor. The developed model’s predictive performance and generalization ability are compared with the response surface methodology, support vector regression, and double-level locally weighted extreme learning machine through the fivefold cross-validation technique. The generalized DLS-SVR predicts gasoline’s RON, BVP, and RVP with R 2 = 0.901, 0.959, and 0.931 and RMSE = 0.055, 0.061, and 0.053, respectively, indicating that its performance is superior to alternative generalized models. The optimal conditions are computed using the DLS-SVR model and co-evolutionary particle swarm optimization algorithm (CPSO). The optimal operation of the reactor yielded a 6.78-unit increase in gasoline RON and a minimum BVP of 0.394 %. The results demonstrate that the proposed DLS-SVR model can accurately predict the variables indicating the quality of isomerate gasoline.

Day-Ahead Scheduling for Economic Dispatch of Combined Heat and Power With Uncertain Demand Response

This paper presents an energy management method for the interconnected operation of power, heat, Combined Heat and Power (CHP) units to settle the Day-Ahead market in the presence of a demand response program (DRP). A major challenge in this regard is the price uncertainty for DRP participants. First, the definitive model of the problem is introduced from the perspective of the Regional Market Manager (RMM) in order to minimize the total supply cost in the presence of TOU program, which is a type of DRP. Furthermore, a market-oriented tensile model is presented in the form of a combination of over-lapping generations (OLG) and price elasticity (PE) formulations to determine the amount of electricity demand in the TOU program. Then, a price uncertainty model of the proposed problem is introduced according to the IGDT risk aversion and risk-taking strategies considering information gap decision theory (IGDT). The above problem is solved through the use of the co-evolutionary particle swarm optimization (C-PSO) algorithm and the proposed model is implemented on a standard seven-unit system for a period of 24 hours.

Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem

With the rapid development of financial market, a growing number of stocks become available on the financial market. How to efficiently select these stocks to achieve higher return and lower risk has become a hot research topic in financial management. This is usually called the portfolio optimization problem (POP). When the cardinality constrained (CC) is added to limit the number of selected stocks to a certain value, the resulting CCPOP is more challenging with the following two difficulties: i) Due to the complexity of CC in finical market, how to efficiently deal with CC in POP to obtain feasible solution is difficult and time-consuming. ii) The objectives of portfolio return and risk always conflict with each other and their relation is difficult to balance. To better deal with above difficulties, this paper focuses on the multi-objective CCPOP (MoCCPOP) and proposes a multiple populations co-evolutionary particle swarm optimization (MPCoPSO) algorithm, which is based on multiple populations for multiple objectives (MPMO) framework and has the following four advantages. Firstly, a hybrid binary and real (HBR) encoding strategy is introduced to better represent the stock selection and the asset weight of the solutions in MoCCPOP. Secondly, a return risk ratio heuristic (R3H) strategy based on the historical return and risk of each stock is proposed as a fast CC handling method to obtain feasible solutions. Thirdly, a new particle update method based on bi-directional local search (BLS) strategy is designed to increase the chance to improve the solution accuracy and to approach the global Pareto front (PF). Last but not least, a hybrid elite competition (HEC) strategy is proposed to assist the archive update, which provides more promising solutions and brings diversity to avoid local PF. The first two strategies help to efficiently deal with the CC challenge, while the last two strategies are efficient in solving the multi-objective challenge. By comparing with some recent well-performing and state-of-the-art multi-objective optimization algorithms, MPCoPSO shows the superior performance in solving the MoCCPOP.

Fuzzy multi-period portfolio selection model with time-varying loss aversion

This paper deals with multi-period portfolio selection problem in a fuzzy environment, in which the effects of investors’ time-varying asymmetric attitudes to losses and gains on portfolio selection are taken into consideration. In the paper, a definition of time-varying loss aversion is presented to describe the aforementioned time-varying asymmetric attitudes to losses and gains. A fuzzy multi-period portfolio selection model based on this new definition of time-varying loss aversion is formulated. Then, an improved co-evolutionary particle swarm optimization (ICPSO) algorithm is designed to solve the proposed model. The comparison analysis between the proposed model and the fixed-parameter loss aversion model is provided to demonstrate the fact that the proposed model can significantly affect the performance of portfolio selection. Finally, a numerical example is given to illustrate the application of the proposed model and the efficiency of the designed algorithm.

Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data

Evolutionary feature selection (FS) methods face the challenge of “curse of dimensionality” when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for FS. The proposed algorithm employs the idea of “divide and conquer” in cooperative coevolutionary approach, but several new developed problem-guided operators/strategies make it more suitable for FS problems. First, a space division strategy based on the feature importance is presented, which can classify relevant features into the same subspace with a low computational cost. Following that, an adaptive adjustment mechanism of subswarm size is developed to maintain an appropriate size for each subswarm, with the purpose of saving computational cost on evaluating particles. Moreover, a particle deletion strategy based on fitness-guided binary clustering, and a particle generation strategy based on feature importance and crossover both are designed to ensure the quality of particles in the subswarms. We apply VS-CCPSO to 12 typical datasets and compare it with six state-of-the-art methods. The experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling FS problems with high dimensionality.

User capacity for uplink SCMA system

Sparse Code Multiple Access (SCMA) is a non-orthogonal multiple access (NOMA) scheme which allows multi-user communication with massive connectivity. In this paper, we consider an uplink SCMA network of limited resources (time/frequency/power). We seek to answer the following question: what is the maximum number of users that can be admitted in uplink SCMA subject to user-rate constraints? This is termed “user capacity” in communication network. We first obtain a closed form for the lower bound of user capacity which is the first contribution of this work. We then study the general problem of optimizing the resource allocation in terms of codebook assignment and power allocation to maximize the number of users, as well as the total sum rate subject to power, codebook and user-rate constraints. This optimization problem turns out to be non-convex and NP-hard; therefore, we propose an iterative algorithm that provides sub-optimal codebook assignment and power allocation which can efficiently solve this problem. Specifically, another contribution of this work is showing that using swap matching for codebook assignment and Difference of two Convex functions (DC) programming for power allocation efficiently solve the problem yielding better results compared with, e.g., cooperative co-evolutionary particle swarm optimization (CCPSO) algorithm or simple equal power and conventional codebook allocation. Finally, we verify our analytical bound on the user capacity via Monte Carlo simulations and show that the proposed resource allocation algorithm provides significant gains in the number of scheduled users in the system with higher sum rate compared with other schemes. The proposed algorithm can achieve an increment in the number of scheduled users of 45%, and 80% compared with CCPSO and simple equal power and regular codebook allocation, respectively. We also compare our proposed swap matching criterion based on sum rate maximization with the two-sided exchange stable swap matching and simulation results show the effectiveness of the proposed criterion for our problem.

A discrete cooperatively coevolving particle swarm optimization algorithm for combinatorial double auctions

A combinatorial double auction is a type of double-side auction which makes buyers and sellers trade goods more conveniently than multiple combinatorial auctions. In this paper, we consider the combinatorial double auction problem in which there are transaction costs, supply constraints and surplus constraints. We formulate the WDP of combinatorial double auction problem as an integer programming problem formulation. The winner determination problem (WDP) in combinatorial double auctions poses a challenge due to computational complexity. Particle swarm optimization (PSO) is a well-known approach to deal with complex optimization problems. In the existing literature, different variants of PSO algorithms have been proposed. However, there still lacks a comparative study on effectiveness of applying different variants of PSO algorithms in combinatorial double auctions. As standard discrete PSO (DPSO) algorithm suffers from premature convergence problem, we adopt a coevolution approach to develop a discrete cooperatively coevolving particle swarm optimization (DCCPSO) algorithm that can scale with the problem. The effectiveness of the proposed algorithm is verified by comparing the results with several variants of PSO algorithms and Differential Evolution algorithms through simulation. Simulation results indicate that the proposed DCCPSO algorithm significantly outperforms these variants of PSO algorithms and Differential Evolution algorithms in most test cases of combinatorial double auctions.

Robust beamforming and spatial precoding for quasi-OSTBC massive MIMO communications

This paper considers the problem of massive multiple-input-multiple-output (MIMO) wireless communication systems with quasi-orthogonal space-time block code (QOSTBC) transmission in the presence of spatial correlation effect (SCE) and mutual coupling effect (MCE). Conventional MIMO channels with QOSTBC transmission suffer from several drawbacks. The number of transmit antennas is restricted to a few in order to achieve full transmission rate. Therefore, it is difficult to make a quasi-orthogonal space time block coded massive MIMO system to achieve full transmission rate. Moreover, MIMO channel with QOSTBC transmission is usually considered for the case where the number of user equipments (UEs) is one. We present a joint beamforming and spatial precoding method to deal with the above drawbacks. The proposed method incorporates a beamforming scheme and spatial precoding to formulate an appropriate optimization process to effectively alleviate the considered problems. The resulting optimization problem will be solved by using a cooperative coevolutionary particle swarm optimization algorithm under two proposed fitness functions. During the optimization process, the proposed method finds the optimal beamforming coefficients of the precoding matrix, the optimal normalized positions of transmit and receive antenna elements for the case of using linear antenna arrays, and the optimal angle differences of transmit and receive antenna elements for the case of using circular arrays. Based on the proposed method, we are able to cure the performance degradation of a quasi-orthogonal space time block coded massive MIMO system due to the SCE and MCE. Moreover, the proposed method makes QOSTBC MIMO communications with full transmission rate for any number of transmit antennas achievable. Several simulation examples are presented to show the superior bit error rate (BER) performances of QOSTBC wireless MIMO scenarios with linear as well as circular antenna arrays by using the proposed method as compared to the existing methods.

A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization

In the hybrid energy system of large green ships, different types of energy sources are employed to feed the electricity demand. An optimal energy-management model and control methodology must be developed to obtain operational safety and efficiency. In this study, optimal power-flow dispatching of maritime photovoltaic/battery/diesel/cold-ironing hybrid energy systems is proposed to sufficiently explore solar energy and minimize the ship’s electricity cost. By modelling the constraints (such as power balance, solar output, diesel output, battery capacity, and regulations from the port) as penalty functions, the optimal energy-management is described as an unconstrained, large-scale, global optimization problem, which can be effectively solved by the proposed adaptive multi-context cooperatively coevolving particle swarm optimization algorithm. The proposed approach is verified by simulation for different cases. Results of the simulation show that the optimal energy-management of the evaluated system can be obtained with great electricity cost savings and robust control performance.

Vector coevolving particle swarm optimization algorithm

In this paper, we propose a novel vector coevolving particle swarm optimization algorithm (VCPSO). In VCPSO, the full dimension of each particle is first randomly partitioned into several sub-dimensions. Then, we randomly assign either one of our newly designed scalar operators or learning operators to update the values in each sub-dimension. The scalar operators are designed to enhance the population diversity and avoid premature convergence. In addition, the learning operators are designed to enhance the global and local search ability. The proposed algorithm is compared with several other classical swarm optimizers on thirty-three benchmark functions. Comprehensive experimental results show that VCPSO displays a better or comparable performance compared to the other algorithms in terms of solution accuracy and statistical results.

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial--hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study.

A Fast and Stable Forecasting Model to Forecast Power Load

As the traditional gray forecasting model GM(1, 1) has poor performance in forecasting the fast-growing power load, we present a chaotic co-evolutionary particle swarm optimization (CCPSO) algorithm, one with better efficiency than the PSO algorithm. Based on the GM(1, 1) model, the CCPSO algorithm is adopted to solve the values of parameters a and b in GM(1, 1) model. This is how the way we come up with the CCPSO algorithm-based GM. As can be seen from experimental results of case simulation on the power consumption in three regions, the CCPSO-GM model is better than the other four forecasting models, which attests its wide applicability and high forecasting accuracy.

Optimal satellite formation reconfiguration using co-evolutionary particle swarm optimization in deep space

This paper proposes a method that plans energy-optimal trajectories for multi-satellite formation reconfiguration in deep space environment. A novel co-evolutionary particle swarm optimization algorithm is stated to solve the nonlinear programming problem, so that the computational complexity of calculating the gradient information could be avoided. One swarm represents one satellite, and through communication with other swarms during the evolution, collisions between satellites can be avoided. In addition, a dynamic depth first search algorithm is proposed to solve the redundant search problem of a co-evolutionary particle swarm optimization method, with which the computation time can be shorten a lot. In order to make the actual trajectories optimal and collision-free with disturbance, a re-planning strategy is deduced for formation reconfiguration maneuver.

Biomimicry of parasitic behavior in a coevolutionary particle swarm optimization algorithm for global optimization

The declining of population diversity is often considered as the primary reason for solutions falling into the local optima in particle swarm optimization (PSO). Inspired by the phenomenon that parasitic behavior is beneficial to the natural ecosystem for the promotion of its biodiversity, this paper presents a novel coevolutionary particle swarm optimizer with parasitic behavior (PSOPB). The population of PSOPB consists of two swarms, which are host swarm and parasite swarm. The characteristics of parasitic behavior are mimicked from three aspects: the parasites getting nourishments from the host, the host immunity, and the evolution of the parasites. With a predefined probability, which reflects the characteristic of the facultative parasitic behavior, the two swarms exchange particles according to particles’ sorted fitness values in each swarm. The host immunity is mimicked through two ways: the number of exchange particles is linearly decreased over iterations, and particles in the host swarm can learn from the global best position in the parasite swarm. Two mutation operators are utilized to simulate two aspects of the evolution of the parasites in PSOPB. In order to embody the law of “survival of the fittest” in biological evolution, the particles with poor fitness in the host swarm are removed and replaced by the same numbers of randomly initialized particles. The proposed algorithm is experimentally validated on a set of 21 benchmark functions. The experimental results show that PSOPB performs better than other eight popular PSO variants in terms of solution accuracy and convergence speed.

Optimal Placement and Sizing of Distributed Generation Units Using Co-Evolutionary Particle Swarm Optimization Algorithms

Today, with the increase of distributed generation sources in power systems, it’s important to optimal location of these sources. Determine the number, location, size and type of distributed generation (DG) on Power Systems, causes the reducing losses and improving reliability of the system. In this paper is used Co-evolutionary particle swarm optimization algorithm (CPSO) to determine the optimal values of the listed parameters. Obtained results through simulations are done in MATLAB software is presented in the form of figure and table in this paper. These tables and figures, show how to changes the system losses and improving reliability by changing parameters such as location, size, number and type of DG. Finally, the results of this method are compared with the results of the Genetic algorithm (GA) method, to determine the performance of each of these methods. DOI: http://dx.doi.org/10.11591/telkomnika.v13i2.7026 Full Text: PDF

Fast and Accurate Optimization of a GPU-accelerated CA Urban Model through Cooperative Coevolutionary Particle Swarms

The calibration of Cellular Automata (CA) models for simulating land-use dynamics requires the use of formal, well-structured and automated optimization procedures. A typical approach used in the literature to tackle the calibration problem, consists of using general optimization metaheuristics. However, the latter often require thousands of runs of the model to provide reliable results, thus involving remarkable computational costs. Moreover, all optimization metaheuristics are plagued by the so called curse of dimensionality, that is a rapid deterioration of efficiency as the dimensionality of the search space increases. Therefore, in case of models depending on a large number of parameters, the calibration problem requires the use of advanced computational techniques. In this paper, we investigate the effectiveness of com- bining two computational strategies. On the one hand, we greatly speed up CA simulations by using general-purpose computing on graphics processing units. On the other hand, we use a specifically designed cooperative coevolutionary Particle Swarm Optimization algorithm, which is known for its ability to operate effectively in search spaces with a high number of dimensions.

Hierarchical Deployment Optimization for Wireless Sensor Networks

Sensor nodes deployment is very crucial for wireless sensor networks (WSNs). Current methods are apt to enlarge the coverage by achieving a nearly even deployment with similar density in the whole network. However, in some specific applications, the even distribution may not satisfy the sensing requirements. This paper proposes a virtual force directed coevolutionary particle swarm optimization (VFCPSO) algorithm, which uses a combined objective function to achieve the tradeoff of coverage and energy consumption. By considering deployment as an optimization problem, VFCPSO is more reliable and flexible for WSNs, since it can satisfy the combined requirements instead of only enlarging coverage. For investigating the performance of different paradigms, centralized VFCPSO is extended to distributed VFCPSO, heterogeneous hierarchical VFCPSO and homogeneous hierarchical VFCPSO (Homo-H-VFCPSO), and the solution of preferential deployment in interested region is also analyzed. Simulation results show that the Homo-H-VFCPSO has the best performance, i.e., it is more efficient than other three VFCPSO algorithms and the VF-style algorithms in terms of computation time, coverage and efficient moving energy consumption. It is obvious that the Homo-H-VFCPSO has good global searching ability and scalability, and it can rapidly and effectively achieve the sensor nodes deployment in WSNs.