Cooperative Evolutionary Algorithm Research Articles

Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis

Large-scale high-dimensional many-objective optimization problems (LaMaOPs) are prevalent in fields such as autonomous driving, cloud resource scheduling, and smart grids. LaMaOPs involve a large number of decision variables and multiple conflicting objectives that need to be optimized simultaneously. The challenges posed by the curse of dimensionality due to the vast number of decision variables, and the conflict between convergence and diversity caused by the numerous objective variables, make traditional optimization methods inadequate. To address these issues, this paper proposes a two-population cooperative evolutionary algorithm based on large-scale decision variable analysis (DVA-TPCEA). This algorithm integrates quantitative analysis methods for decision variables to deeply examine their impact on each objective and introduces a contribution-based objective detection method. Additionally, a dual-population cooperative evolution mechanism is employed, with targeted optimization strategies designed for convergence and diversity populations, achieving synergistic and complementary optimization between the two populations. To validate the algorithm’s effectiveness in practical applications, a large-scale container resource scheduling strategy based on the DVA-TPCEA algorithm is also proposed. The experimental results indicate that the proposed algorithm demonstrates significant advantages in both general datasets DTLZ, WFG, and LSMOP, and practical models.

Symmetrical Generalized Pareto Dominance and Adjusted Reference Vector Cooperative Evolutionary Algorithm for Many-Objective Optimization

Symmetrical Generalized Pareto Dominance and Adjusted Reference Vector Cooperative Evolutionary Algorithm for Many-Objective Optimization

Dual-stage and dual-population cooperative evolutionary algorithm for solving constrained multiobjective problems

During the search process, the characteristics of the feasible regions encountered by the population continually change in Constrained Multiobjective Optimization Problems (CMOPs). This variability poses a challenge for traditional evolutionary algorithms, which often struggle to adapt to the diverse problem characteristics of the encountered feasible regions. To overcome this limitation, we propose a Dual-Stage and Dual-Population Cooperative Evolutionary Algorithm (DDCEA) to address CMOPs characterized by diverse feasible regions. DDCEA employs a dual-stage mechanism to adapt the offspring generation strategy and establishes two distinct populations to evaluate offspring using constraint-sensitive and constraint-free strategies. Comparative analyses reveal that DDCEA surpasses chosen state-of-the-art CMOEAs in adapting to the changing feasible regions and then approximating the constrained Pareto fronts.

A cooperative evolutionary algorithm with simulated annealing for integrated scheduling of distributed flexible job shops and distribution

Production and distribution are two essential parts in supply chains. An integration of production and distribution has received amount of attention from both academia and industry. This article investigates an integrated scheduling problem of distributed flexible job shops and distribution. First, a mixed integer programming model is developed to minimize maximum completion time. Second, a cooperative evolutionary algorithm with simulated annealing methods is proposed in consideration of the problem's characteristics. A three-link integer string is designed to represent three parts, i.e., factory assignment, machine assignment and operation sequence, in a solution. Three populations are constructed to search the above three parts via genetic operations and simulated annealing methods, respectively. A cooperation strategy is designed to generate a schedule. Then, a heuristic rule is devised to determine the delivery scheme for distribution. Finally, experiments are carried out via extending a set of benchmark instances. The proposed algorithm is compared to four state-of-the-art algorithms and an exact solver CPLEX. The results and discussions verify that the developed model and method have strong competitiveness for solving the considered problems.

A Cooperative Evolutionary Computation Algorithm for Dynamic Multiobjective Multi-AUV Path Planning

Multiple autonomous underwater vehicles (AUVs) are popular for executing submarine missions, which involve multiple targets distributed in a large and complex underwater environment. The path planning of multiple AUVs is a significant and challenging problem, which determines the location of surface points for AUV launch and plans the paths of AUVs for target traveling. Most existing works model the problem as a single-objective static optimization problem. However, the target missions may change over time, and multiple optimization objectives are usually expected for decision making. Thus, this paper models the problem as a dynamic multiobjective optimization problem and proposes a cooperative evolutionary computation algorithm to provide diverse and high-quality solutions for decision makers. In the proposed method, solutions are represented using a bi-layer encode scheme, in which the first layer indicates the surface location points and the second layer represents the traveling sequences of target missions. Multiple populations for multiple objectives (MPMO) framework is adopted to efficiently solve the dynamic multiobjective AUV optimization problem. In addition, a recombination-based sampling strategy is developed to improve convergence by fusing the information of multiple populations. Once a change occurs, an incremental response strategy is adopted to generate high-quality solutions for population evolution. Based on the dataset of New Zealand bathymetry, six complex underwater scenarios are constructed with a size of 50 km×50 km×10 km and 400 target missions for tests. Experimental results show that the proposed method outperforms the state-of-the-art algorithms in terms of solution diversity and optimality.

A Branch-and-Bound Enhanced Cooperative Evolutionary Algorithm for the Hybrid Seru System Scheduling Considering Worker Heterogeneity

A Branch-and-Bound Enhanced Cooperative Evolutionary Algorithm for the Hybrid Seru System Scheduling Considering Worker Heterogeneity

Learning-based two-phase cooperative optimizer for distributed machine scheduling with heterogeneous factories and order priorities

In the realm of customized manufacturing, production cycles are often compressed to capture market opportunities swiftly. The blanking system stands as the inaugural and pivotal phase in the realm of large equipment customized manufacturing. This study abstracts a novel problem from real-world blanking systems, as the distributed unrelated parallel machine scheduling with heterogeneous factories and order priorities (DUPMS-HP). The presented work formulates the bi-objective DUPMS-HP, aiming to minimize both the total weighted tardiness and the workload gap of each machine. A learning-based two-phase cooperative optimizer (LCTPO) is introduced to address this NP-hard problem, featuring: i) a cooperative evolutionary algorithm during the first stage for global search to ensure diversity; ii) the incorporation of five problem-specific local search strategies in the first stage to balance priority and due date constraints. Additionally, reinforcement learning is applied to learn and select the best neighborhood search operator for each elite solution, further enhancing diversity. The effectiveness of the proposed algorithm is validated through a comparative analysis with five state-of-the-art algorithms on 20 instances. Experimental results affirm that LCTPO is more adept at solving DUPMS-HP compared to the alternative algorithms.

Joint Resource Scheduling for UAV-Enabled Mobile Edge Computing System in Internet of Vehicles

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications

A dual-population algorithm based on self-adaptive epsilon method for constrained multi-objective optimization

Balancing multiple objectives and various constraints is crucial for effectively solving constrained multi-objective optimization problems (CMOPs). Excessive focus on either convergence or feasibility may not result in favorable outcomes of the algorithm. To confront this challenge, this paper proposes a cooperative evolutionary algorithm named SaE-CMO, which aims to achieve a harmonious balance between convergence and feasibility by extracting valuable information from both feasible and infeasible regions. To achieve this, SaE-CMO employs a dual-population approach to enhance search progress, consisting of a main population, Population1, and an auxiliary population, Population2. These two populations complement each other to achieve optimal results. A newly proposed self-adaptive epsilon method is employed in both Population1 and Population2, using different comparison criteria to select next population from mating pools, respectively. Population2 can retain some solutions that are well-constrained but poorly converged, thereby preserving information about both the constrained and the unconstrained Pareto front. This property enables Population2 to assist Population1 in maintaining diversity in certain complex CMOPs. To verify the effectiveness of SaE-CMO, we conduct experiments on three benchmark test instances and four real-world CMOPs with some related state-of-the-art constrained multi-objective optimization algorithms, experimental results prove that the proposed algorithm outperforms the compared algorithms.

Efficient Elitist Cooperative Evolutionary Algorithm for Multi-Objective Reinforcement Learning

Efficient Elitist Cooperative Evolutionary Algorithm for Multi-Objective Reinforcement Learning

A two-stage cooperative evolutionary algorithm for energy-efficient distributed group blocking flow shop with setup carryover in precast systems

As the main link of the rapidly developing prefabricated construction industry, the production of precast components (PCs) has become a research hotspot. Therefore, the distributed group flow shop scheduling problem with blocking and carryover sequence-dependent setup time constraints (DPGFSP-BCT) in precast systems is considered. To address this problem, first, a mixed integer linear model is presented. Second, a two-stage cooperative coevolutionary algorithm (TS-CCEA) is proposed to minimize both the makespan and total energy consumption (TEC). In TS-CCEA, two acceleration rules are designed to reduce computational efforts. Third, to diversify the population, different initialization methods are established for different populations. Based on the problem-specific knowledge of solution classification, the individuals of the group population and job population execute two neighborhood search algorithms. Subsequently, considering the TEC, a critical path based speed mutation strategy is proposed to further improve the exploitation ability. Furthermore, a reinitialization heuristic is developed to avoid premature convergence. Last, the performance of the TS-CCEA is verified after calibrating the parameters. The experimental results demonstrate the stability and effectiveness of the algorithm.

Joint optimization of preventive maintenance and production rescheduling with new machine insertion and processing speed selection

With the continuous improvement of machine performance, the multifunction machine has gradually become the first choice of many enterprises. Due to the usage and wearing of the machine, the successive operating times will decrease, while the consecutive maintenance durations will increase. Under these contexts, this paper focus on the joint optimization of preventive maintenance and flexible job-shop rescheduling with processing speed selection, and the dynamic arrival of the new machine is considered to enhance productivity. Specifically, (1) a bivariate maintenance policy considering processing speed is proposed to develop a reasonable maintenance plan for each machine; and (2) for evaluating, responding and optimizing the dynamic scheduling problem, a multi-objective optimization model, two rescheduling strategies and a bi-population cooperative evolutionary algorithm are separately constructed. In numerical experiments, the Taguchi method is first employed to set the parameters of the proposed algorithm. Second, the superiority of the proposed migration rescheduling strategy is demonstrated by comparing complete rescheduling strategy. Third, the effectiveness of the improved operators and the proposed algorithm is verified by algorithm comparison. Next, the benefits of the selectable processing speed are proven by comparing with the nominal processing speed. Finally, a sensitivity analysis on the processing speed optional range is performed.

An elitist cooperative evolutionary bi-level multi-objective decomposition-based algorithm for sustainable supply chain

Many real-life applications are modelled using hierarchical decision-making in which: an upper-level optimisation task is constrained by a lower-level one. Such class of optimisation problems is referred in the literature as Bi-Level Optimisation Problems (BLOPs). Most of the proposed methods tackled the single-objective continuous case adhering to some regularity assumptions. This is at odds with real-world problems which involve mainly discrete variables and expensive objective function evaluations. Besides, the optimisation process becomes exorbitantly time-consuming, especially when optimising several objectives at each level. For this reason, the Multi-objective variant (MBLOP) remains relatively less explored and the number of methods tackling the combinatorial case is much reduced. Motivated by these observations, we propose in this work an elitist decomposition-based evolutionary algorithm to solve MBLOPs, called ECODBEMA. The basic idea of our proposal is to handle, decomposition, elitism and multithreading mechanisms to cope with the MBLOP's high complexity. ECODBEMA is applied to the production–distribution problem and to a sustainable end-of-life products disassembly case-study based on real-data of Aix-en-Provence French city. We compared the optimal solutions of an exact method using CPLEX solver with near-optimal solutions obtained by ECODBEMA. The statistical results show the significant outperformance of ECODBEMA against other multi-objective bi-level optimisation algorithms.

A Two-Stage Cooperative Evolutionary Algorithm With Problem-Specific Knowledge for Energy-Efficient Scheduling of No-Wait Flow-Shop Problem.

Green scheduling in the manufacturing industry has attracted increasing attention in academic research and industrial applications with a focus on energy saving. As a typical scheduling problem, the no-wait flow-shop scheduling has been extensively studied due to its wide industrial applications. However, energy consumption is usually ignored in the study of typical scheduling problems. In this article, a two-stage cooperative evolutionary algorithm with problem-specific knowledge called TS-CEA is proposed to address energy-efficient scheduling of the no-wait flow-shop problem (EENWFSP) with the criteria of minimizing both makespan and total energy consumption. In TS-CEA, two constructive heuristics are designed to generate a desirable initial solution after analyzing the properties of the problem. In the first stage of TS-CEA, an iterative local search strategy (ILS) is employed to explore potential extreme solutions. Moreover, a hybrid neighborhood structure is designed to improve the quality of the solution. In the second stage of TS-CEA, a mutation strategy based on critical path knowledge is proposed to extend the extreme solutions to the Pareto front. Moreover, a co-evolutionary closed-loop system is generated with ILS and mutation strategies in the iteration process. Numerical results demonstrate the effectiveness and efficiency of TS-CEA in solving the EENWFSP.

A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem

In the era of big data, the size and complexity of the data are increasing especially for those stored in remote locations, and whose difficulty is further increased by the ongoing rapid accumulation of data scale. Real-world optimization problems present new challenges to traditional intelligent optimization algorithms since the traditional serial optimization algorithm has a high computational cost or even cannot deal with it when faced with large-scale distributed data. Responding to these challenges, a distributed cooperative evolutionary algorithm framework using Spark (SDCEA) is first proposed. The SDCEA can be applied to address the challenge due to insufficient computing resources. Second, a distributed quantum-behaved particle swarm optimization algorithm (SDQPSO) based on the SDCEA is proposed, where the opposition-based learning scheme is incorporated to initialize the population, and a parallel search is conducted on distributed spaces. Finally, the performance of the proposed SDQPSO is tested. In comparison with SPSO, SCLPSO, and SALCPSO, SDQPSO can not only improve the search efficiency but also search for a better optimum with almost the same computational cost for the large-scale distributed optimization problem. In conclusion, the proposed SDQPSO based on the SDCEA framework has high scalability, which can be applied to solve the large-scale optimization problem.

Evolutionary Human-UAV Cooperation for Transmission Network Restoration

Power transmission networks are vulnerable to natural or man-made disasters, and it is of critical importance to efficiently restore damaged power supply in disaster-affected areas. A large-scale damaged transmission network can contain many faults that are initially uninspected/unlocated. Using unmanned aerial vehicles (UAVs) to inspect these faults can significantly improve the efficiency of subsequent restoration performed by human operators. Such a cooperative human-UAV scheduling problem is highly complex due to the correlation between UAV schedules and human-team schedules. In this article, we propose a cooperative evolutionary algorithm that simultaneously evolves two populations, one of UAV scheduling solutions (U-solutions) and the other of human-team scheduling solutions (H-solutions), which cooperate by determining a best matching U-solution for each H-solution and evaluating U-solutions based on a surrogate objective function that is iteratively improved by feedback from H-solutions. Our algorithm exhibits significant performance advantages over the state-of-the-arts on various test instances and an application to transmission network restoration in the 2017 Jiuzhaigou earthquake.

Cooperative evolutionary classification algorithm for hyperspectral images

Reliable crop maps are a vital source of information for many economic sectors. Hyperspectral images and efficient supervised classification algorithms can help in obtaining these maps. One of the objectives is to use a spectroradiometer as a source of a priori information for training supervised classification algorithms. Although there are many supervised classification algorithms, few can be trained using collected spectral signatures, such as spectral angle mapper (SAM). This algorithm relies on setting the angle threshold values manually to separate different classes, and it is usually a random process. So, the second objective is to use an automated process to optimize the angle threshold values to improve the efficiency of the SAM algorithm. An innovative cooperative classification algorithm for hyperspectral images based on enhancing the supervised SAM algorithm performance using a genetic algorithm (GA) is presented. The improvement is based on selecting global optimal threshold angle values for different classes using GA. The efficiency of the developed cooperative evolutionary algorithm is proved by classifying hyperspectral images to create maps of major crops, such as wheat, potato, and alfalfa. The source of the hyperspectral images is the Compact High-Resolution Imaging Spectrometer onboard of the Proba satellite. The evaluation of the results showed that the new cooperative evolutionary algorithm classified hyperspectral images with the highest accuracy compared to well-known reliable supervised classification algorithms.

Cooperative-Evolution-Based WPT Resource Allocation for Large-Scale Cognitive Industrial IoT

The recently developed technique of wireless power transfer (WPT) provides a promising way to charge the wireless sensor networks (WSNs) of cognitive industrial Internet of Things (IoT) deployed in areas that are difficult for humans to access. Previous work has focused on the power allocation strategy at the wireless node level. However, the priority among different modes in an identical wireless node has not been taken into consideration, and different modes equipped with different types of batteries accomplish different tasks in an identical wireless node. One challenging scenario is rechargeable WSNs with a large number of wireless nodes. In this article, we aim to optimize the power allocation strategy in priority constraint WPT systems with a large number of wireless nodes. Traditional WPT systems consist of a rechargeable WSN and a mobile charger, which are deployed for charging wireless nodes in a wireless manner. However, the constructed WPT system consists of a rechargeable WSN and multiple mobile chargers with adequate power, which can charge wireless nodes simultaneously. Each solution of the power allocation strategy can be represented as one disjunctive graph, and the critical path (CP) in the disjunctive graph is the core factor in determining the final maximum cost. Thus, we propose a decomposition strategy that can identify the interacting variables based on the CP by exploiting the perturbation technique. Then, the decomposed subcomponents are cooperatively evolved by adopting a cooperative evolutionary algorithm (CEA). The proposed CP-based grouping strategy combined with CEA is named CPCEA. Three state-of-the-art methods are tested and compared with CPCEA, and three scales of datasets are considered. The experimental results demonstrate the validity of CPCEA.

Nonlinear dynamic system identification with a cooperative population-based algorithm featuring a restart metaheuristic

Dynamic system identification is commonly reduced to an optimization problem which is complex and multimodal. To find a global optimum of this problem, evolutionary algorithms are often applied. However, as it was shown in many studies, conventional evolution-based algorithms do not demonstrate the acceptable performance for this class of problems, therefore, some effective modifications have been proposed so far. In our study, we combine two approaches which were previously used in linear dynamic system identification and allowed their accurate identification. More specifically, we present a cooperative evolutionary algorithm with a restart metaheuristic and apply it for the parameter identification of a nonlinear cascaded system. The experimental results prove the effectiveness of the proposed evolution-based identification compared to other known solutions of this problem.

Multi-Scenario Cooperative Evolutionary Algorithm for the β-Robust p-Median Problem with Demand Uncertainty

In this paper, we studied the solution approach for the β-robust p-median problem with a large number of scenarios for the uncertain demands. The concept of neighborhood scenarios was introduced to describe the scenarios with a higher similarity than others. By utilizing knowledge from the solutions of neighborhood scenarios and the parallel search strategy, a novel multi-scenario cooperative evolutionary algorithm was proposed to solve the problem for all scenarios in one run. The proposed algorithm was compared with the widely used location–allocation heuristic and genetic algorithm through two practical cases, which were a network with 95 cities and a network with 668 demand nodes in an urban area. The computational results indicate that our algorithm can obtain better solutions in a much shorter time.