Cooperative Coevolutionary Genetic Algorithm Research Articles

Research on the Dynamic Synergy Mechanism of Industry-Education Integration and Science-Education Integration in the Construction of a Strong Educational Country

Abstract The purpose of this paper is to combine the integration of industry and education with the fusion of science and education and to promote the acceleration of the construction of a strong educational country under the perspective of collaborative governance. On the basis of exploring the traditional genetic algorithm, combined with the synergistic relationship between the integration of industry and education and the integration of science and education, the cooperative co-evolutionary genetic algorithm (CCGA) is selected to analyze the synergistic optimization of the two. Combined with the CCGA algorithm process, the synergistic efficiency and cooperative stability of the two are explored, and the coordination degree of the two is measured using the method of example analysis. Among them, the island model genetic algorithm and CCGA algorithm are selected for function testing to verify the superiority of this paper’s algorithm. The data shows that the synergy degree of elevator installation, repair, and maintenance majors in School Q is 0.48, ranking first. 0.44 For construction, 0.27 for electromechanical. 0.06 for agriculture, and the integration degree of other majors is lower than the average of 9 majors. The degree of synergy between industry, education and science in this university needs to be improved, and the connection between the education chain and the industrial chain needs to be strengthened.

A cooperative co-evolutionary genetic algorithm for query recommendation

A cooperative co-evolutionary genetic algorithm for query recommendation

Target Tracking Algorithm Based on Generalized Regression Neural Network for Passive Bistatic Radar

In passive bistatic radar, high-performance target tracking is of crucial importance to a variety of applications. However, most existing tracking algorithms cannot achieve satisfactory performance due to poor azimuth estimation and the nonlinear relationship between target position and measurements. This paper proposes a novel tracking algorithm based on improved generalized regression neural networks (GRNN) and Kalman filtering (KF). First, the dependency between measurements and the target position is modeled by GRNN regression. A cooperative co-evolutionary genetic algorithm (CCGA) is then introduced to optimize the sample size and smoothing factor of the GRNN model to improve the performance. Finally, this paper applies KF to CCGA-GRNN localization results and forms the smoothing trajectories of the targets. Simulation and experimental results show that the performance of the proposed algorithm is better than that of the geometric localization (GL), GRNN, and extreme learning machine (ELM) based tracking approaches. Furthermore, field experiment data verifies the feasibility of the proposed algorithm.

Optimization of service scheduling problem for overlapping tower cranes with cooperative coevolutionary genetic algorithm

PurposeIn regarding to operational efficiency and safety improvements, multiple tower crane service scheduling problem is one of the main problems related to tower crane operation but receives limited attention. The current work presents an optimization model for scheduling multiple tower cranes' service with overlapping areas while achieving collision-free between cranes.Design/methodology/approachThe cooperative coevolutionary genetic algorithm (CCGA) was proposed to solve this model. Considering the possible types of cross-tasks, through effectively allocating overlapping area tasks to each crane and then prioritizing the assigned tasks for each crane, the makespan of tower cranes was minimized and the crane collision avoidance was achieved by only allowing one crane entering the overlapping area at one time. A case study of the mega project Daxing International Airport has been investigated to evaluate the performance of the proposed algorithm.FindingsThe computational results showed that the CCGA algorithm outperforms two compared algorithms in terms of the optimal makespan and the CPU time. Also, the convergence of CCGA was discussed and compared, which was better than that of traditional genetic algorithm (TGA) for small-sized set (50 tasks) and was almost the same as TGA for large-sized sets.Originality/valueThis paper can provide new perspectives on multiple tower crane service sequencing problem. The proposed model and algorithm can be applied directly to enhance the operational efficiency of tower cranes on construction site.

Integrated production planning and preventive maintenance scheduling for synchronized parallel machines

Production synchronization is an emerging paradigm in advanced manufacturing systems, with which the inventory space and cost of finished products can be significantly reduced. This paradigm has been studied in the past years, and several mathematical models have been developed to evaluate production synchronization for a diversity of manufacturing systems. Most of the existing studies on production synchronization, however, assume that machines are always functioning without malfunctions in production process. It is no doubt that machine failures can definitely lead to unexpected breakdowns that may produce non-ignorable impact on production synchronization. In this study, a new evaluation model for production synchronization is put forth for parallel machines. The production time uncertainty caused by unexpected breakdowns of machines is explicitly quantified, and the distribution of the job completion time on a machine is approximated by the Gamma distribution through the moment match method. By integrating production planning and preventive maintenance scheduling together, a new joint optimization problem is formulated for synchronized parallel machines, and it is resolved by a modified cooperative co-evolutionary genetic algorithm with a heuristic initialization. Two illustrative examples along with a set of comparative studies are presented to examine the effectiveness of the proposed model and solution algorithm.

Cooperative Coevolution-based Design Space Exploration for Multi-mode Dataflow Mapping

Some signal processing and multimedia applications can be specified by synchronous dataflow (SDF) models. The problem of SDF mapping to a given set of heterogeneous processors has been known to be NP-hard and widely studied in the design automation field. However, modern embedded applications are becoming increasingly complex with dynamic behaviors changes over time. As a significant extension to the SDF, the multi-mode dataflow (MMDF) model has been proposed to specify such an application with a finite number of behaviors (or modes) and each behavior (mode) is represented by an SDF graph. The multiprocessor mapping of an MMDF is far more challenging as the design space increases with the number of modes. Instead of using traditional genetic algorithm (GA)-based design space exploration (DSE) method that encodes the design space as a whole, this article proposes a novel cooperative co-evolutionary genetic algorithm (CCGA)-based framework to efficiently explore the design space by a new problem-specific decomposition strategy in which the solutions of node mapping for each individual mode are assigned to an individual population. Besides, a problem-specific local search operator is introduced as a supplement to the global search of CCGA for further improving the search efficiency of the whole framework. Furthermore, a fitness approximation method and a hybrid fitness evaluation strategy are applied for reducing the time consumption of fitness evaluation significantly. The experimental studies demonstrate the advantage of the proposed DSE method over the previous GA-based method. The proposed method can obtain an optimization result with 2×−3× better quality using less (1/2−1/3) optimization time.

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.

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.

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.

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.

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.

Cooperative Coevolution with Dynamic Species-Size Strategy for Vibration-Based Damage Detection in Plates

Vibration-based damage detection is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage occurs. This paper proposes dynamic species-size strategy in cooperative coevolution concept. The resulting algorithm, cooperative coevolutionary genetic algorithm with dynamics species-size (CCGADSS), is used as the optimization algorithm in the vibration-based damage detection in plates. The objective function is numerically calculated from the difference between experimentally vibration characteristics and numerically evaluated vibration characteristics of the predicted damage. In finite element model for objective calculation, the plates are equally divided into 64 elements. There are 2 different cases with dissimilar occurred damage in plates are considered. In first case, the plate hase only one region consisting of 4 elements which are together connected and have same damage. In second case, there are 5 separated elements which are damaged differently. In order to demonstrate the performance of the dynamic species-size strategy, 3 optimization algorithms, which are genetic algorithm (GA), cooperative coevolutionary genetic algorithm (CCGA), and CCGADSS. The results indicate that CCGADSS is superior to GA and CCGA. Moreover solutions obtained using CCGADSS are quite close the actual damage. These results show that the dynamic species-size strategy can enhance performance of cooperative coevolution concept.

Comparison of Fuzzy System with Neural Aggregation FSNA with Classical TSK Fuzzy System in Anti-Collision Problem of USV

Abstract The paper presents the research whose the main goal was to compare a new Fuzzy System with Neural Aggregation of fuzzy rules FSNA with a classical Takagi-Sugeno-Kanga TSK fuzzy system in an anti-collision problem of Unmanned Surface Vehicle USV. Both systems the FSNA and the TSK were learned by means of Cooperative Co-evolutionary Genetic Algorithm with Indirect Neural Encoding CCGA-INE. The paper includes an introduction to the subject, a description of the new FSNA and the tuning method CCGA-INE, and at the end, numerical research results with a summary. The research includes comparison of the FSNA with the classical TSK system in the anti-collision problem of the USV.

Evaluation of water resources system vulnerability based on co-operative co-evolutionary genetic algorithm and projection pursuit model under the DPSIR framework

Water resources vulnerability control management is essential because it is related to the benign evolution of socio-economic, environmental and water resources system. Research on water resources system vulnerability is helpful to realization of water resources sustainable utilization. In this study, the DPSIR framework of driving forces-pressure–state–impact-response was adopted to construct the evaluation index system of water resources system vulnerability. Then the co-evolutionary genetic algorithm and projection pursuit were used to establish evaluation model of water resources system vulnerability. Tengzhou City in Shandong Province was selected as a study area. The system vulnerability was analyzed in terms of driving forces, pressure, state, impact and response on the basis of the projection value calculated by the model. The results show that the five components all belong to vulnerability Grade II, the vulnerability degree of impact and state were higher than other components due to the fierce imbalance in supply-demand and the unsatisfied condition of water resources utilization. It is indicated that the influence of high speed socio-economic development and the overuse of the pesticides have already disturbed the benign development of water environment to some extents. While the indexes in response represented lower vulnerability degree than the other components. The results of the evaluation model are coincident with the status of water resources system in the study area, which indicates that the model is feasible and effective.

Ship Pipe Routing Method Based on Genetic Algorithm and Cooperative Coevolution

Pipe routing is one of the most time-consuming and complicated jobs in shipbuilding design. This article presents the automatic ship pipe routing method. To improve the efficiency of single pipe routing, the fixed-length encoding genetic algorithm (GA) is first used by connecting adjacent intermediate points with generated pipe segments according to the specific routing patterns. The crossover and mutation operations are designed on the basis of this encoding as well. In case of the routing for multi pipes or pipe with branches, cooperative coevolutionary GA is adopted to route pipes harmoniously and to reduce the risk of combinatorial explosion caused by the number of pipes. During algorithm implementation and the building of cell decomposition model, the practical constraints in ship piping have been taken into account. In the end, the efficiency and feasibility of the proposed approach are illustrated by solving problems in designed test case and real ship applications.

A Cooperative Co-Evolutionary Genetic Algorithm for Tree Scoring and Ancestral Genome Inference.

Recent advances of technology have made it easy to obtain and compare whole genomes. Rearrangements of genomes through operations such as reversals and transpositions are rare events that enable researchers to reconstruct deep evolutionary history among species. Some of the popular methods need to search a large tree space for the best scored tree, thus it is desirable to have a fast and accurate method that can score a given tree efficiently. During the tree scoring procedure, the genomic structures of internal tree nodes are also provided, which provide important information for inferring ancestral genomes and for modeling the evolutionary processes. However, computing tree scores and ancestral genomes are very difficult and a lot of researchers have to rely on heuristic methods which have various disadvantages. In this paper, we describe the first genetic algorithm for tree scoring and ancestor inference, which uses a fitness function considering co-evolution, adopts different initial seeding methods to initialize the first population pool, and utilizes a sorting-based approach to realize evolution. Our extensive experiments show that compared with other existing algorithms, this new method is more accurate and can infer ancestral genomes that are much closer to the true ancestors.

Topology and sizing optimization of discrete structures using a cooperative coevolutionary genetic algorithm with independent ground structures

This article proposes a method called the cooperative coevolutionary genetic algorithm with independent ground structures (CCGA-IGS) for the simultaneous topology and sizing optimization of discrete structures. An IGS strategy is proposed to enhance the flexibility of the optimization by offering two separate design spaces and to improve the efficiency of the algorithm by reducing the search space. The CCGA is introduced to divide a complex problem into two smaller subspaces: the topological and sizing variables are assigned into two subpopulations which evolve in isolation but collaborate in fitness evaluations. Five different methods were implemented on 2D and 3D numeric examples to test the performance of the algorithms. The results demonstrate that the performance of the algorithms is improved in terms of accuracy and convergence speed with the IGS strategy, and the CCGA converges faster than the traditional GA without loss of accuracy.

CCGA-BN Constructor: A Bayesian Network Learning Approach

This paper presents a tool CCGA-BN Constructor for learning Bayesian network that uses cooperative coevolutionary genetic algorithm to learn Bayesian network structure from data. The problem has been broken down into two sub-problems: (a) to find the optimal nodes’ordering and (b) to find the optimal adjacency matrix of the graph. Both the sub-problems’ solutions are then combined to produce the optimal structure. CCGA-BN constructor used Bayesian score for networks having nodes with more than two states and BIC for network having bistate nodes. The findings of this paper are compared against the original structures and the results show a lot of promise.