Coevolutionary Genetic Algorithm Research Articles

Применение коэволюционных генетических алгоритмов в задачах установления места контакта объектов исследования в экспертизе ДТП

This article considers the method of applying coevolutionary genetic algorithms when performing an expert study to determine the place of vehicles` collision or the place of a vehicle`s collision with a pedestrian in the reconstruction of a road accident. The results of calculation by the proposed method and calculation by the generally accepted method are compared.

Vibration Control Method of an Electromagnetic Isolation System Based on LQR and Coevolutionary NGA

An electromagnetic isolation system can dynamically adjust the output characteristic parameters of the system in real time through the active control strategy, which has strong adaptability to the external environment. In order to control the electromagnetic vibration isolation system effectively, an active control method is presented based on the linear quadratic regulator (LQR) approach and the coevolutionary niche genetic algorithm (NGA). In this paper, the dynamical equation and state equation of the electromagnetic isolation system are built, which include the nonlinear relationship between electromagnetic force and coil current and gap. The LQR approach is employed to maintain a steady state of an isolated object on the vibration isolation system. Meanwhile, a coevolutionary niche genetic algorithm is put forward to optimize the parameters in Q and R matrices. Simulation and experimental results demonstrate that the electromagnetic isolation system with the LQR approach and coevolutionary NGA can effectively isolate the vibration and maintain a steady state for an isolated object in comparison with the passive isolation system.

A co-evolutionary genetic algorithm for the two-machine flow shop group scheduling problem with job-related blocking and transportation times

This study investigates a new two-machine flow shop group scheduling problem with job-related blocking and transportation times, which is derived from the realistic pipe-making process of steel pipe products in the modern steel manufacturing industry. In contrast to the traditional blocking constraint, the attributes of jobs, not the quantity of jobs in the buffer area, are used to determine the need for a blocking feature. The objective is to minimize the makespan. We present a mixed integer linear programming model and prove that the problem is strongly NP-hard. As the problem is a joint decision of two sub-problems, namely group scheduling and job scheduling within each group, a co-evolutionary genetic algorithm (CGA) is proposed to solve it. In the proposed CGA, the two sub-problems are synergistically evolved via a co-evolutionary framework. A block-mining-based artificial chromosome construction strategy is designed to speed up the convergence process. Computational experiments based on actual production data are carried out. The results indicate that the proposed CGA is effective for the considered problem.

Load-Frequency Control of Multi-Area Power System Based on the Improved Weighted Fruit Fly Optimization Algorithm

With the development and application of large-scale renewable energy sources, the electric power grid is becoming huge and complicated; one of the most concerning problems is how to ensure coordination between a large number of varied controllers. Differential games theory is used to solve the problem of collaborative control. However, it is difficult to solve the differential game problem with constraints by using conventional algorithm. Furthermore, simulation models established by existing research are almost linear, which is not conducive to practical engineering application. To solve the above problem, we propose a co-evolutionary algorithm based on the improved weighted fruit fly optimization algorithm (IWFOA) to solve a multi-area frequency collaborative control model with non-linear constraints. Simulation results show that the control strategy can achieve system control targets, and fully utilize the various characteristics of each generator to balance the interests of different areas. Compared with a co-evolutionary genetic algorithm and a collaborative multi-objective particle swarm optimization algorithm, the co-evolutionary algorithm based on the IWFOA has a better suppression effect on the frequency deviation and tie-line power deviation caused by the disturbance and has a shorter adjustment time.

A Multi-Objective Model for Minimizing Makespan and Total Travel Time in Put Wall Based Picking Systems

In this study, a multi-objective order batching and picker routing model for minimising the total travel time and makespan in a put wall-based picking system is proposed. A put wall is a hardware composed of containers that is usually used as a sorting station. Put walls usually have limited capacity and can accommodate only a part of received orders at a time. Thus, in put wall-based picking systems, order batching and picker routing should be considered along with the scheduling of the orders on the put wall. To solve the problem, a genetic algorithm (GA), a coevolutionary genetic algorithm (COGA) and an archived multi-objective simulated annealing (AMOSA) are proposed. It is found that GA and COGA offer a more diverse set of solutions to the problem compared to AMOSA.

Genetic Programming-Based Code Generation for Arduino

This article describes a methodology for writing the program for the Arduino board using an automatic generator of assembly language routines that works based on a cooperative coevolutionary multi-objective linear genetic programming algorithm. The methodology is described in an illustrative example that consists of the development of the program for a digital thermometer organized on a circuit formed by the Arduino Mega board, a text LCD module, and a temperature sensor. The automatic generation of a routine starts with an input-output table that can be created in a spreadsheet. The following routines have been automatically generated: initialization routine for the text LCD screen, routine for determining the temperature value, routine for converting natural binary code into unpacked two-digit BCD code, routine for displaying a symbol on the LCD screen. The application of this methodology requires basic knowledge of the assembly programming language for writing the main program and some initial configuration routines. With the application of this methodology in the illustrative example, 27% of the program lines were written manually, while the remaining 73% were generated automatically. The program, produced with the application of this methodology, preserves the advantage of assembly language programs of generating machine code much smaller than that generated by using the Arduino programming language.

Application of Dueling DQN and DECGA for Parameter Estimation in Variogram Models

Parameter estimation of variogram models is an important problem in geostatistics and environmental engineering. Most of existing works aim to estimate parameters of variogram single models, while neglecting the parameter estimation of variogram nested model. Most recently, the evolutionary algorithms(EA), including genetic algorithm(GA), are exploited to calculate the parameters of variogram model, which can obtain a more accurate solution. These methods have some hyper-parameters to set and suffer from the well-recognized premature convergence and slow global convergence problem of EA. In this paper, a double elite co-evolutionary genetic algorithm(DECGA) and deep reinforcement learning(dueling DQN) was introduced to estimate the parameters of variogram single or nested models so as to achieve better generalization performance. The DECGA can get the global optimal solution faster than GA with the help of dueling DQN, which can set the hyper-parameters according to the state of DECGA. To verify the effectiveness of the proposed method(DDQNGA), we conduct experiments on the agricultural heavy metal database. Experimental results demonstrate that our method can obtain parameter estimation more accurately. The method proposed in this paper have a certain practical value in the field of geostatistics and environmental engineering.

Efficiently Generating Test Data to Kill Stubborn Mutants by Dynamically Reducing the Search Domain

Mutation testing is a fault-oriented software testing technique, and a test suite generated based on the criterion of mutation testing generally has a high capability in detecting faults. A mutant that is hard killed is called a stubborn one. The traditional methods of test data generation often fail to generate test data that kill stubborn mutants. To improve the efficiency of killing stubborn mutants, in this article, we propose a method of generating test data by dynamically reducing the search domain under the criterion of strong mutation testing. To fulfill this task, we first present a method of measuring the stubbornness of a mutant based on the reachability condition of a mutated statement. Then, we formulate the problem of generating test data to kill the mutant as an optimization one with a unique constraint. Finally, we generate test data using a coevolutionary genetic algorithm. Given the fact that the domain of test data that kills a stubborn mutant is generally small, we adopt a method of dynamically reducing the search domain to improve the efficiency of the algorithm. We apply the proposed method to test eight benchmark and industrial programs. The experimental results demonstrate that the proposed method has capabilities in seeking stubborn mutants and efficiently generating test data to kill stubborn mutants.

Optimal planning of PEV fast charging stations using an auction-based method

In recent years, the plug-in electric vehicles (PEVs) have attracted a lot of attention. For integration of PEVs, proper planning of PEV charging stations is necessary. Location and size of fast charging stations (FCSs) are very important for distribution companies (DISCOs) and FCS investors. In this paper, FCS planning problem is modeled as a mixed-integer nonlinear programming (MINLP) from DISCO point of view. The proposed method is based on the dynamic auction, and a co-evolutionary genetic algorithm is used to solve the problem. FCS investors bid DISCO for energy in different locations. The DISCO, as the auctioneer, determines the location and size of FCSs based on the bids received at each location so that its profit is maximized. In the proposed model, capacitor and feeder installations are considered to satisfy the bus voltage limit and the apparent power capacity constraint of feeders, respectively. The effectiveness of the proposed method is demonstrated by numerical results. Our work shows that the proposed method can be used as a proper tool for fast FCS optimal planning in the presence of private investors of FCSs. Furthermore, in the new environment of power systems, dynamic auctions can be utilized in FCS planning. In addition, the capacitor and feeder installations can be effectively used for getting a better value of DISCO objective function.

Path Planning for Multi-UAV Formation Rendezvous Based on Distributed Cooperative Particle Swarm Optimization

This paper studies the problem of generating cooperative feasible paths for formation rendezvous of unmanned aerial vehicles (UAVs). Cooperative path-planning for multi-UAV formation rendezvous is mostly a complicated multi-objective optimization problem with many coupled constraints. In order to satisfy the kinematic constraints, i.e., the maximum curvature constraint and the requirement of continuous curvature of the UAV path, the Pythagorean hodograph (PH) curve is adopted as the parameterized path because of its curvature continuity and rational intrinsic properties. Inspired by the co-evolutionary theory, a distributed cooperative particle swarm optimization (DCPSO) algorithm with an elite keeping strategy is proposed to generate a flyable and safe path for each UAV. This proposed algorithm can meet the kinematic constraints of UAVs and the cooperation requirements among UAVs. Meanwhile, the optimal or sub-optimal paths can be obtained. Finally, numerical simulations in 2-D and 3-D environments are conducted to demonstrate the feasibility and stability of the proposed algorithm. Simulation results show that the paths generated by the proposed DCPSO can not only meet the kinematic constraints of UAVs and safety requirements, but also achieve the simultaneous arrival and collision avoidance between UAVs for formation rendezvous. Compared with the cooperative co-evolutionary genetic algorithm (CCGA), the proposed DCPSO has better stability and a higher searching success rate.

Scenario-based planning of fast charging stations considering network reconfiguration using cooperative coevolutionary approach

Abstract In recent years, plug-in electric vehicles (PEVs) have attracted a great deal of attention because of environmental issues. For large-scale integration of PEVs, appropriate siting and sizing of PEV charging stations are essential. In this paper, a framework for simultaneous fast charging stations (FCSs) planning and distribution network reconfiguration is proposed. The distribution network reconfiguration is effectively used to optimize objective functions. The objective function considered is defined as the sum of the initial investment and energy loss costs subject to different distribution system constraints. In the model, a scenario-based approach is used for taking into account uncertainties of traditional electric load, FCS load, and electricity price. To reduce the computational burden of the problem, a scenario reduction technique is utilized. A cooperative coevolutionary genetic algorithm (CCGA) is employed to solve the problem. The FCSs locations and the network switches are coded separately, and they evolve cooperatively as two different species, i.e., the location species and the switch species. Furthermore, an efficient technique called Dijkstra's algorithm is used to calculate FCSs sizes in which the nearest FCS to PEVs is determined based on their driving distance from FCSs on their path to their destination. Finally, the performance and the effectiveness of the proposed method are demonstrated by numerical results. More specifically, use of the proposed scenario-based approach yields to a more accurate solution than deterministic method. In addition, usage of network reconfiguration in FCSs planning both reduces the total costs and improves the voltage profile.

A co-evolutionary matheuristic for the car rental capacity-pricing stochastic problem

When planning a selling season, a car rental company must decide on the number and type of vehicles in the fleet to meet demand. The demand for the rental products is uncertain and highly price-sensitive, and thus capacity and pricing decisions are interconnected. Moreover, since the products are rentals, capacity “returns”. This creates a link between capacity with fleet deployment and other tools that allow the company to meet demand, such as upgrades, transferring vehicles between locations or temporarily leasing additional vehicles.We propose a methodology that aims to support decision-makers with different risk profiles plan a season, providing good solutions and outlining their ability to deal with uncertainty when little information about it is available. This matheuristic is based on a co-evolutionary genetic algorithm, where parallel populations of solutions and scenarios co-evolve. The fitness of a solution depends on the risk profile of the decision-maker and its performance against the scenarios, which is obtained by solving a mathematical programming model. The fitness of a scenario is based on its contribution in making the scenario population representative and diverse. This is measured by the impact the scenarios have on the solutions.Computational experiments show the potential of this methodology regarding the quality of the solutions obtained and the diversity and representativeness of the set of scenarios generated. Its main advantages are that no information regarding probability distributions is required, it supports different decision-making risk profiles, and it provides a set of good solutions for an innovative complex application.

Research on optimal collaborative method for microgrid environmental and economic dispatch in grid-connected mode

In recent years, real-time optimisation of microgrid dispatch with minimal total costs of energy generation and emission has been an urgent problem. In this paper, a collaborative method was considered for microgrid connected mode. At first, we constructed the microgrid schematic diagram of energy flow. Then, a mathematical model was presented for dispatch optimisation environmentally and economically. After defining the objective function according to the energy consumption, maintenance and systemic carbon emissions, two types of constraints were given including the single input-output balance and the overall energy flow balance. Thirdly, we analysed the cooperative co-evolutionary genetic algorithm and applied it to a large number of cases. The better optimisation performance and convergence rate were obtained by comparing the method with the traditional genetic algorithms. The experimental results show it may play a significant role in the optimal scheduling of distributed energy generations in grid-connected mode.

Nesting Scheduling in Sheet Metal Processing Based on Coevolutionary Genetic Algorithm in Different Environments

In sheet metal processing, nesting and scheduling are important factors affecting the efficiency and agility of manufacturing. The objective of nesting is to minimize the waste of material, while that of scheduling is to optimize the processing sequence. As the relation between them often becomes a trade-off, they should be considered simultaneously for the efficiency of the total manufacturing process. In this study, we propose a co-evolutionary genetic algorithm-based nesting scheduling method. We first define a cost function as a fitness value, and then we propose a grouping method that forms gene groups based on the processing layout and processing time. Finally, we validate the effectiveness of the proposed method through computational experiments.

Research on QoS service composition based on coevolutionary genetic algorithm

Traditional genetic algorithms overemphasize the struggle for survival and neglect all other aspects of biology. In addition, binary encoding is widely used in individual coding. Since the individual chromosomes produced are longer in length, it is difficult to ensure the efficiency of the algorithm. In this study, a coevolutionary genetic algorithm is proposed for web service composition based on quality of service (QoS), which fully considers the individual relationships among populations. The real coding method is adopted to solve the service selection problem based on QoS, so that the negative effect of the long length of chromosomes in the algorithm is avoided. Moreover, in view of the difficulty of determining the weight of each QoS attribute in web services, we propose to use the entropy method to determine the weights of each one. Compared with the traditional genetic algorithm, the experimental results show that the proposed algorithm converges faster in the service composition, and the fitness of the optimal solution is higher.

Sistema anticolisión para invidentes usando redes neuronales evolutivas

Introducción: El presente artículo muestra el diseño e implementación de un sistema anticolisión para invidentes usando redes neuronales evolutivas.
 Objetivo: Presentar la implementación de redes neuronales evolutivas en un sistema guía para invidentes en la detección de obstáculos estáticos y en movimiento.
 Metodología: La metodología empleada se basa en la creación de redes neuronales artificiales a partir del algoritmo genético cooperativo coevolutivo (AGCC), este se encarga de estructurar, modificar y entrenar las redes neuronales. Para ello utiliza la matriz de definición de red (MDR). Para la elaboración de una MDR se toma como base un cromosoma “parte del algoritmo genético”. Una vez este realizada la MDR se crea una red neuronal artificial para luego ser entrenada.
 Resultados: El programa realizó varias redes neuronales generando en cada ejecución 10 cromosomas, que al ser entrenados con el AGCC y aplicando la cooperatividad, se obtuvieron las mejores redes neuronales anticolisión teniendo en cuenta un tiempo definido, funcionando efectivamente para la detección de obstáculos estáticos y en movimiento.
 Conclusiones: En el sistema anticolisión para invidentes se observó la eficacia de las redes neuronales en dar una respuesta, detectando objetos tanto estáticos como en movimiento proporcionando seguridad al invidente, evitando colisiones con estos.

A Cooperative Co-Evolutionary Optimisation Model for Best-Fit Aircraft Sequence and Feasible Runway Configuration in a Multi-Runway Airport

A careful arrival and departure sequencing of aircraft can reduce the inter-arrival/departure time, thereby opening up opportunities for new landing and/or take-off slots, which may increase the runway throughput. This sequence when serviced with a suitable runway configuration may result in an optimal aircraft sequence with a runway configuration that can process the maximum number of aircraft within a given time interval. In this paper, we propose a Cooperative Co-evolutionary Genetic Algorithm (CCoGA) to find the combined solution of a best-fit sequence with a feasible runway configuration for a given traffic demand at an airport. The aircraft sequence and the runway configuration are modelled as individual species, which can cooperatively interact with each other. Therefore, we computationally evolve the best possible combination of aircraft sequence (arrival and departure) and the feasible runway configuration. The proposed CCoGA algorithm is evaluated for Chicago O’Hare International Airport runway layout and resulting configurations. Arrival and departure traffic demand is modelled through a Poisson distribution. Two different arrival/departure sequencing methods, i.e., constraint position shifting with one, two and N-position shifting and first come first serve, are modelled. Runway configuration and traffic sequence (arrivals and departure) are modelled as two species, which are evolved co-operatively, through the CCoGA algorithm, to achieve the optimal traffic sequencing with a feasible runway configuration. Time-space diagrams are presented for the best-evolved population of arrival-departure sequence and runway configuration to illustrate the possibility of using available departure slots between arrivals to maximize capacity. Arrival-departure capacity envelopes are then presented to illustrate the trade-off between the arrivals and departures, given a runway configuration for each sequencing method. Results demonstrate the high mutual dependence between arrival-departure sequence and the runway configuration, as well as its effect on overall runway capacity. The results also demonstrate the viability of using evolutionary computation-based methods for modelling and evaluating complex problems in the air transport domain.

Time-Cost Efficient Scheduling Algorithms for Executing Workflow in Infrastructure as a Service Clouds

Cloud Computing enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. The cloud infrastructures can be used as an appropriate environment for executing of workflow applications. To execute workflow applications in this environment, it is necessary to develop the workflow scheduling algorithms that consider different QoS parameters such as execution time and cost. Therefore, in this paper we focus on two criteria: total completion time (makespan) and execution cost of workflow, and propose two heuristic algorithms: MTDC (Minimum Time and Decreased Cost) which aims to create a schedule that minimizes the makespan and decreases execution cost, and CTDC (Constrained Time and Decreased Cost) which is based on the first algorithm (MTDC) and aims to create a schedule that decreases the execution cost while satisfying the deadline constraint of the workflow application. The proposed algorithms are evaluated by a simulation process using WorkflowSim. To evaluate the proposed algorithms, the results of MTDC are compared with the results of HEFT (Heterogeneous Earliest Finish Time), and the results of CTDC are compared with the results of heuristic based algorithms [such as IC-PCP (IaaS Cloud Partial Critical Paths), IC-PCPD2 (Deadline Distribution) and BDHEFT (Budget and Deadline HEFT)] and meta-heuristic based algorithms [such as PSO (Particle Swarm Optimization) and CGA2 (Coevolutionary Genetic Algorithm with Adaptive penalty function)]. The results show that the proposed algorithms perform better than the mentioned algorithms in most cases.

Optimization of DP-M-QAM Transmitter Using Cooperative Coevolutionary Genetic Algorithm

We present a method for joint optimization of transmitter in-phase, quadrature and interpolarization time skew, amplitude mismatch, and bias voltages. The method is based on a cooperative coevolutionary genetic algorithm with fitness functions extracted from a directly detected reference quadrature amplitude modulation (QAM) signal generated at the transmitter. As a calibration method, it is able to find the values that will statically generate the best possible constellation. To the extent of the simulation investigations conducted, the algorithm is capable to calibrate time skews, bias voltages, IQ phase imbalances, and relative amplitude imbalances with standard deviation of residual error as low as 0.24 ps, 0.019 V, 0.56°, and 0.003, respectively, for a dual-polarization IQ modulator with $V_{\pi } = \text{4}\,\text{V}$ and a 16-QAM reference signal operating at 16 GBd. An experimental demonstration is also reported.

基于双种群协同进化遗传算法的电力仓库货位分配方法

基于双种群协同进化遗传算法的电力仓库货位分配方法