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 co-evolutionary genetic algorithm with knowledge transfer for multi-objective capacitated vehicle routing problems

The capacitated vehicle routing problem (CVRP) aims to find the routes that minimize transportation costs. However, in real-world applications, the equitable workload is essential for the drivers and logistics companies. For assigning an appropriate load for each vehicle, this paper formulates the CVRP with workload balancing as a multi-objective optimization problem (MO-CVRP). The goal is to simultaneously minimize the total length of routes and the variance of loads assigned to each vehicle. Although several multi-objective evolutionary algorithms (MOEAs) have been proposed for the MO-CVRP, they often suffer from slow convergence speeds due to the complexity of the search space, which hinders the identification of clear search directions. To address this issue, the knowledge transfer method is introduced that can leverage search experiences from related problems to expedite the solution process of the current problem. Specifically, a co-evolutionary genetic algorithm with knowledge transfer is proposed, where a simplified version of the traveling salesman problem (TSP) by disregarding the vehicle capacity constraint is utilized to facilitate the quick solution of the original MO-CVRP. To enable information interaction between these two problems, the widely used denoising autoencoder is adopted to construct a transformation relation between solutions from the TSP to the MO-CVRP. Additionally, an adaptive migration strategy is proposed to adaptively select the migration methods and an improved split operator strategy is designed to ensure the feasibility of the solutions. The proposed algorithm is compared with four state-of-the-art MOEAs on MO-CVRP test sets with different scales. Experimental results show that the proposed algorithm exhibits faster convergence speed and achieves solutions with superior convergence.

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.

Using Coevolution and Substitution of the Fittest for Health and Well-Being Recommender Systems

This research explores substitution of the fittest (SF), a technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. SF is domain independent and requires no calibration. We first perform a controlled comparative evaluation of SF’s ability to maintain engagement and discover optimal solutions in a minimal toy domain. Experimental results demonstrate that SF performs similarly to alternative techniques presented in the literature but has the advantage of requiring no parameter tuning. We then address the more complex real-world problem of evolving recommendations for health and well-being. We introduce a coevolutionary extension of EvoRecSys, a previously published evolutionary recommender system. We demonstrate that SF is able to maintain a better trade-off between engagement and performance than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.

A Refined Collaborative Scheduling Method for Multi-Equipment at U-Shaped Automated Container Terminals Based on Rail Crane Process Optimization

A U-shaped automated container terminal (ACT) has been proposed for the first time globally and has been adopted to construct the Beibu Gulf Port ACT. In this ACT layout, the double cantilevered rail crane (DCRC) simultaneously provides loading and unloading services for the external container trucks (ECTs) and the automatic guided vehicles (AGVs) entering the yard. The DCRC has a complex scheduling coupling relationship with the AGV and the ECT, and its mathematical model is extremely complex. There is an urgent need to study a practical collaborative scheduling optimization model and algorithm for the DCRC, the AGV, and the ECT. In this paper, we optimize the process flow of DCRCs to study the refined collaborative scheduling model of DCRCs, AGVs and ECTs in U-shaped ACTs. Firstly, we analyze the operation process of the DCRC and divide the 16 loading and unloading conditions of the DCRC into four operation modes for process optimization. Secondly, different variables and parameters are set for the DCRC’s four operating modes, and a refined collaborative dispatching model for the DCRCs with AGVs and ECTs is proposed. Finally, a practical adaptive co-evolutionary genetic algorithm solves the model. Meanwhile, arithmetic examples verify the correctness and practicality of the model and algorithm. The experimental results show that the total running time of the DCRCs is the shortest in the U-shaped ACT when the number of quay cranes (QC) to DCRC and AGV ratios are 1:2 and 1:10, respectively. At the same time, the number of QCs and DCRCs has a more significant impact on the efficiency of the ACT than that of AGVs, and priority should be given to the allocation of QCs and DCRCs. The research results have essential guidance value for U-shaped ACTs under construction and enrich the theory and method of collaborative scheduling of U-shaped ACT equipment.

Cooperative Area Search Algorithm for Multi-UAVs in Uncertain Battlefield Environment

Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperative area search in uncertain battlefield environment, a multi-UAVs cooperative area search algorithm based on co-evolutionary genetic algorithm (CEGA) is proposed. Firstly, the raster Map and extended Search Probability Map (SPM) model of the task area are constructed, the UAV state model and sensor detection model are established, and the search environment updating method based on the Bayesian criterion is presented. Then, considering the cooperative elements of multi-UAVs, the cooperative area search planning model is established, and based on the rolling optimization idea, the co-evolutionary genetic algorithm with elite retention strategy is used to optimize the solution, and the multi-UAVs cooperative search paths are generated. Through simulation experiments and comparative analysis, the effectiveness of the proposed algorithm is verified.

A co-evolutionary genetic algorithm for robust and balanced controller placement in software-defined networks

The controller placement problem (CPP) is one of the main issues that need to be addressed in the context of Software Defined Networking (SDN), especially when different aspects are being considered, such as latency, capacity, reliability, and load balancing. Most of the solutions in the literature address these aspects by considering a fixed load for each controller and attempting to equally distribute the traffic demand of the switches among the controllers, which also have a fixed common capacity. On the contrary, in this work, the CPP is studied by considering load, controller capacity, and the failure probability of controllers and links as varying over time. The CPP is formulated in terms of a robust optimization problem, which, by introducing the concept of scenario, takes into account changes in the network status due to failures, load variations, and changes in switches’ demand and controllers’ capacity. The provided solution is robust, that is, neither controllers’ re-placement nor switches’ re-assignment is required as network conditions change. Besides, a co-evolutionary algorithm is provided to solve the aforementioned optimization problem. Two populations coevolve based on the concept of complementary evolution of allied species in nature. Experimental results on a set of real-world network topologies and comparisons with the state-of-the-art have proven the superiority of the proposal, in terms of better latency, load balancing and resilience, in solving the CPP under different network status changes that might occur over time.

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.

Robust Optimization Design for Path Planning of Bionic Robotic Fish in the Presence of Ocean Currents

The bionic robotic fish is one of the special autonomous underwater vehicles (AUV), whose path planning is crucial for many applications including underwater environment detection, archaeology, pipeline leak detection, and so on. However, the uncertain ocean currents increase the difficulty of path planning for bionic robotic fish in practice. In this paper, the path energy consumption is selected as the objective function for path planning, path safety factor, and smoothness are considered as the constraint conditions. The kinematic model is established for bionic robotic fish and, considering the uncertainty of ocean currents, a “min-max” robust optimization problem is proposed in the light of the normal optimization model of path planning for bionic robotic fish. The co-evolutionary genetic algorithm is presented to solve the robust optimization problem with two populations; one population represents the solutions and the other represents the uncertain ocean currents. The objective of the proposed algorithm is to find a robust solution that has the best worst-case performance over a set of possible ocean currents. Multiple experiments indicate that the proposed algorithm is very effective for path planning for bionic robotic fish with ocean currents.

A co-evolutionary genetic algorithms approach to detect video game bugs

Video games systems are known for their complexity, concurrency and non-determinism, which makes them prone to challenging tacit bugs. Video games development is costly and the corresponding verification process is tiresome. Testing the nondeterministic and concurrent behaviors of video games systems is not only crucial but also challenging, especially when the game state space is huge. Accordingly, typical software testing approaches are neither suitable nor effective to find related bugs. Novel automated approaches to support video game testing are needed. This problem has caught researchers’ attention recently. Approaches found in the literature have tried to address two sub problems: modeling and uncovering bugs. Colored Petri nets is known to support modeling and verifying concurrent and nondeterministic systems. Search approaches have been used in the literature to check the availability of faulty states through exploring state spaces. However, these approaches tend to lack adaptability to test different video games systems due to the limitations of the defined fitness functions, in addition to difficulties in searching huge state spaces due to exhaustive and unguided search. The availability of automated approaches that guide and direct the process of bugs finding is mandatory. Thus, in this study we address this problem as we present a solution for automated software testing using collaborative work of two genetic algorithms (i.e. co-evolutionary) agents, where our approach is applied to colored Petri nets representations of the software workflow. The results of our experiments have shown the potential of the proposed approach in effectively finding bugs automatically.

The benefits of co-evolutionary Genetic Algorithms in voyage optimisation

Reducing emissions is of increasing global importance. Within shipping, the International Maritime Organisation’s regulations are putting pressure on companies to quickly reduce emissions. One solution is the optimisation of a ship’s route where even comparatively small reductions, in the order of 5%, provide sizeable cost and environmental benefits. The most recent advances from the Evolutionary Computation field have not been benchmarked on this problem, especially the co-evolutionary algorithms that provide the widest diversity of search. This paper compares state-of-the-art algorithms on three case studies, to show the impact of algorithm selection on the fuel consumption and expected voyage time. Four state-of-the-art Genetic Algorithms are selected to represent the leading families of Genetic Algorithm. The co-evolutionary approaches are shown to have the top performance, with cMLSGA (co-evolutionary Multi-Level Selection Genetic Algorithm) showing top performance on all the problems with the greatest potential reductions in fuel usage, 7.6% on average over the state of the art, and voyage times, 8.4% on average over the state of the art.

Application of Multi-Objective Optimization Problem Based on Genetic Algorithm

With the rise and rapid development of mobile communications, smart terminals and data mining technologies, we are entering the mobile Internet era. When dealing with the multi-objective problem encountered in the network, it cannot control the operation strategy of various specific applications by itself. Therefore, a fault-tolerant mechanism is needed to provide a high degree of flexibility at the application level. When the mobile database changes, only the error handling strategy needs to be modified without considering the specific code of the application. This paper mainly introduces the application research of multi-objective optimization problem based on genetic algorithm. In this paper, the application research of multi-objective optimization problems based on genetic algorithm is used, and the multi-objective evolutionary algorithm still needs to be improved in terms of convergence and distribution. Some algorithms with better convergence performance often have slightly insufficient distribution performance, but the distribution performance is relatively low. The best algorithm often needs to improve its convergence performance. Therefore, it is of great significance to study the multi-objective co-evolution genetic algorithm. The experimental results of this paper show that the application research for the multi-objective optimization problem based on genetic algorithm increases the optimization rate of multi-objective problem by 14%, and the limitation of the multi-objective optimization problem of genetic algorithm provides good indoor path planning for the application of genetic algorithm. Analysis, discussion and summary of the methods and approaches to enrich the academic research results.

Channel power optimization in WDM systems using co-evolutionary genetic algorithm

In this work, we present a co-evolutionary genetic (CEGA) algorithm to adapt the optical launch powers and optimize the signal-to-noise ratio (SNR) values based on maximizing the minimum SNR margin. The introduced co-evolutionary algorithm provides lower computational complexity rather than convex optimization and linear programming techniques, applicable for both static and time-critical dynamic networking. The enhanced Gaussian noise nonlinear model is exploited to take the physical-layer impairments into account, considering networks with partial spectrum utilization. To optimize the minimum SNR margin, we formulate the power allocation problem as a minimax optimization problem. To this end, a two-space genetic algorithm (GA) is proposed to reduce the computational complexity. The obtained results demonstrate that the introduced co-evolutionary algorithm outperforms the common optimization methods in terms of run time. It is shown that the computational complexity of proposed co-evolutionary algorithm is significantly lower than convex and single-space evolutionary approaches by several orders of magnitude. Moreover, the minimum SNR margin is improved by about 2.4 dB compared to a flat launch power optimization.

A competitive Predator–Prey approach to enhance surveillance by UAV swarms

In this paper we present the competitive optimisation of a swarm of Unmanned Aerial Vehicles (UAV) protecting a restricted area from a number of intruders following a Predator–Prey approach. We propose a Competitive Coevolutionary Genetic Algorithm (CompCGA) which optimises the parameters of the UAVs (i.e. predators) to maximise the detection of intruders, while the parameters of the intruders (i.e. preys) are optimised to maximise their intrusion success rate. Having chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) as the base mobility model for the UAVs, we propose an improved new version, where its behaviour is modified by identifying and optimising new parameters to improve the overall success rate when detecting intruders. Six case studies have been optimised using simulations by performing 30 independent runs (180 in total) of our CompCGA. Finally, we conducted a series of master tournaments (1,800,000 evaluations) using the best specimens obtained from each run and case study to test the robustness of our proposed approach against unexpected intruders. Our surveillance system improved the average percentage of intruders detected with respect to CACOC by a maximum of 126%. More than 90% of intruders were detected on average when using a swarm of 16 UAVs while CACOC’s detection rates are always under 80% in all cases.

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.

UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance.

In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi-Swarms). CROMM-MS is designed for controlling the trajectories of heterogeneous multi-swarms of aerial, ground and marine unmanned vehicles with important features such as prioritising early detections and success rate. A new Competitive Coevolutionary Genetic Algorithm (CompCGA) is proposed to optimise the vehicles’ parameters and escapers’ evasion ability using a predator-prey approach. Our results show that CROMM-MS is not only viable for surveillance tasks but also that its results are competitive in regard to the state-of-the-art approaches.

Allocation and Scheduling of Handling Resources in the Railway Container Terminal Based on Crossing Crane Area

The integrated allocation and scheduling of handling resources are crucial problems in the railway container terminal (RCT). We investigate the integrated optimization problem for handling resources of the crane area, dual-gantry crane (GC), and internal trucks (ITs). A creative handling scheme is proposed to reduce the long-distance, full-loaded movement of GCs by making use of the advantages of ITs. Based on this scheme, we propose a flexible crossing crane area to balance the workload of dual-GC. Decomposing the integrated problem into four sub-problems, a multi-objective mixed-integer programming model (MIP) is developed. By analyzing the characteristic of the integrated problem, a three-layer hybrid heuristic algorithm (TLHHA) incorporating heuristic rule (HR), elite co-evolution genetic algorithm (ECEGA), greedy rule (GR), and simulated annealing (SA) is designed for solving the problem. Numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. The results show that the proposed algorithm has excellent searching ability, and the simultaneous optimization scheme could ensure the requirements for efficiency, effectiveness, and energy-saving, as well as the balance rate of dual-GC.

A Constrained Static Scheduling Strategy in Edge Computing for Industrial Cloud Systems

With the development of industrial internet, attention has been paid for edge computing due to the low latency. However, some problems remain about the task scheduling and resource management. In this paper, an edge computing supported industrial cloud system is investigated. According to the system, a constrained static scheduling strategy is proposed to over the deficiency of dynamic scheduling. The strategy is divided into the following steps. Firstly, the queue theory is introduced to calculate the expectations of task completion time. Thereupon, the task scheduling and resource management problems are formulated and turned into an integer non-linear programming (INLP) problem. Then, tasks that can be scheduled statically are selected based on the expectation of task completion and constrains of various aspects of task. Finally, a multi-elites-based co-evolutionary genetic algorithm (MEB-CGA) is proposed to solve the INLP problem. Simulation result shows that the MEB-CGA significantly outperforms the scheduling quality of greedy algorithm.