Coevolutionary Algorithm Research Articles

A dual-population coevolutionary algorithm for balancing convergence and diversity in the decision space in multimodal multi-objective optimization

Many multimodal multi-objective evolutionary algorithms (MMEAs) are effective in solving multimodal multi-objective problems (MMOPs), which have multiple equivalent Pareto optimal sets (PSs) mapping to the same Pareto optimal front (PF). Due to the existence of the global convergence-first mechanism, these MMEAs will remove the solutions that can improve the diversity of the decision space but have poor convergence and even lead to the loss of PS when encountering MMOPs with an imbalance between convergence and diversity in the decision space (MMOP-ICD) or an MMOP with a local PS (MMOPL). We propose a new dual-population coevolutionary algorithm to address these issues. The auxiliary population helps the main population locate areas where equivalent PSs may exist, and the main population focuses on balancing convergence and diversity in the decision space. When updating the auxiliary population, a strength local convergence quality (SLCQ) is used to explore the distribution of the equivalent PSs. When updating the main population, the new niche-based truncation strategy first deletes the solutions that contribute less to convergence. Then, a distance-based subset selection method balances the diversity between the decision and objective spaces. The comparison results show the overall performance of the proposed algorithm is significantly better than other state-of-the-art algorithms.

A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection

In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. This paper introduces a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) for feature selection, especially for high-dimensional features. In the proposed algorithm, we make improvements to the foundational dominance-based multi-objective evolutionary algorithm in two aspects. First, the use of feature correlation as knowledge to guide evolution enhances the search speed and quality of traditional multi-objective evolutionary algorithm solutions. Second, a dynamically allocated competitive–cooperative evolutionary mechanism is proposed, integrating the improved knowledge-guided evolution with traditional evolutionary algorithms, further enhancing the search efficiency and diversity of solutions. Through rigorous empirical testing on various datasets, the KCCEA demonstrates superior performance compared to basic multi-objective evolutionary algorithms, providing effective solutions to multi-objective feature selection problems while enhancing the interpretability and effectiveness of prediction models.

Runtime Analysis of Competitive Co-evolutionary Algorithms for Maximin Optimisation of a Bilinear Function

Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by pathological behaviour, such as loss of gradient, relative over-generalisation, and mediocre objective stasis. It is an open challenge to develop a theory that can predict when co-evolutionary algorithms find solutions efficiently and reliable. This paper provides a first step in developing runtime analysis for population-based competitive co-evolutionary algorithms. We provide a mathematical framework for describing and reasoning about the performance of co-evolutionary processes. To illustrate the framework, we introduce a population-based co-evolutionary algorithm called PDCoEA, and prove that it obtains a solution to a bilinear maximin optimisation problem in expected polynomial time. Finally, we describe settings where PDCoEA needs exponential time with overwhelmingly high probability to obtain a solution.

A two-stage bidirectional coevolution algorithm with reverse search for constrained multiobjective optimization

Constrained multiobjective optimization problems (CMOPs) are widespread in reality. The presence of constraints complicates the feasible region of the original problem and increases the difficulty of problem solving. There are not only feasible regions, but also large areas of infeasible regions in the objective space of CMOPs. Inspired by this, this paper proposes a bidirectional coevolution method with reverse search (BCRS) combined with a two-stage approach. In the first stage of evolution, constraints are ignored and the population is pushed toward promising regions. In the second stage, evolution is divided into two parts, i.e., the main population evolves toward the constrained Pareto front (CPF) within the feasible region, while the reverse population approaches the CPF from the infeasible region. Then a solution exchange strategy similar to weak cooperation is used between the two populations. The experimental results on benchmark functions and real-world problems show that the proposed algorithm exhibits superior or at least competitive performance compared to other state-of-the-art algorithms. It demonstrates BCRS is an effective algorithm for addressing CMOPs.Graphical

Multi-period fourth-party logistics network design from the viability perspective: a collaborative hyper-heuristic embedded with double-layer Q-learning algorithm

In the ‘new normal’ setting of a mega-crisis, the viability becomes the driving force for the fourth party logistics (4PL) network design. In this paper, the viability is characterised in terms of agility, resilience and survival sustainability as the response to changes in demand, disruption and survivability. The fortification and recovery strategies are considered in possible disruptions at transfer centres and third-party logistics providers. A novel mixed integer non-linear programming model is proposed to obtain the multi-period 4PL network solution with minimum total cost under viability constraints. Considering the NP-hard characteristic of problem and the non-convex of proposed model, the hyper-heuristic algorithm is designed. To take advantage of both global optimality seeking and local search ability, a collaborative hyper-heuristic embedded with double-layer Q-learning (CHHDLQL) algorithm is proposed. The effectiveness and efficiency of the proposed algorithm is demonstrated by the promising numerical results. By stress-testing the existing network, appropriate adjustments to fortification and recovery strategies can effectively cope with changes in demand and disruption. Furthermore, the impact of 4PL strategy, fortification and recovery strategies, and viability constraints are investigated. The demand satisfaction, network resilience and capacity can be improved by adjusting agility, resilience and survival sustainability to influence different component network costs. Highlights A novel multi-period 4PL network model under viability constraints is presented. The CHHDLQL algorithm for construction and perturbation coevolution is proposed. The stress tests apply to the 4PL network, including demand, disruption and 3PL. 4PL improves demand satisfaction, resilience and capacity by adjusting viability.

Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA

This paper presents a two-step approach for optimizing the configuration of a mobile photovoltaic-diesel-storage microgrid system. Initially, we developed a planning configuration model to ensure a balance between the mobility of components and a sustainable power supply. Then, we introduced a method that merges optimization and decision-making. The first phase identifies Pareto optimal solutions (POSs) with a favorable distribution by using a multi-objective evolutionary algorithm with classification-based preselection (CPS-MOEA). In the second phase, we utilize the fuzzy C-means algorithm (FCM) and the grey relational projection (GRP) method for comprehensive decision-making. This aims to select the most suitable and compromise solution from the POSs, closely aligning with the decision-maker’s preferences. Beyond addressing the optimal planning and configuration issue, the experimental results show that the method surpasses other widely used multi-objective optimization algorithms, including the Preference Inspired Co-evolution Algorithm (PICEA-g), the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO), and the third stage of Generalized Differential Evolution (GDE3).

A Knowledge-Based Cooperative Co-Evolutionary Algorithm for Non-Contact Voltage Measurement

A Knowledge-Based Cooperative Co-Evolutionary Algorithm for Non-Contact Voltage Measurement

Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret

In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand adversarial attacks, including one of the most widely adopted, efficient, and interpretable ML algorithms—decision trees (DTs). This paper proposes a novel coevolutionary algorithm (CoEvoRDT) designed to create robust DTs capable of handling noisy high-dimensional data in adversarial contexts. Motivated by the limitations of traditional DT algorithms, we leverage adaptive coevolution to allow DTs to evolve and learn from interactions with perturbed input data. CoEvoRDT alternately evolves competing populations of DTs and perturbed features, enabling construction of DTs with desired properties. CoEvoRDT is easily adaptable to various target metrics, allowing the use of tailored robustness criteria such as minimax regret. Furthermore, CoEvoRDT has potential to improve the results of other state-of-the-art methods by incorporating their outcomes (DTs they produce) into the initial population and optimize them in the process of coevolution. Inspired by the game theory, CoEvoRDT utilizes mixed Nash equilibrium to enhance convergence. The method is tested on 20 popular datasets and shows superior performance compared to 4 state-of-the-art algorithms. It outperformed all competing methods on 13 datasets with adversarial accuracy metrics, and on all 20 considered datasets with minimax regret. Strong experimental results and flexibility in choosing the error measure make CoEvoRDT a promising approach for constructing robust DTs in real-world applications.

A co-evolutionary algorithm based on sparsity clustering for sparse large-scale multi-objective optimization

Sparse large-scale multi-objective optimization problems (LSMOPs), which are characterized by high dimensional search space and sparse Pareto optimal solutions, have a widespread existence in academic research and practical applications. While the high dimensional decision space poses challenges to multi-objective evolutionary algorithms (MOEAs), the difficulty of solving sparse LSMOPs can be alleviated by utilizing the prior knowledge that the optimal solutions are sparse. In this paper, a co-evolutionary algorithm based on sparsity clustering, namely SCEA, is proposed, where the prior knowledge of sparse optimal solutions is utilized explicitly. At each generation, SCEA first calculates the current optimal sparsity by sparsity clustering. Then, SCEA divides the population into a winner subpopulation and two loser subpopulations. While the winner subpopulation reproduces offspring solutions by conventional genetic operators, the loser subpopulations generate offspring solutions along two competitive directions under the guidance of current optimal sparsity and variable importance. In the experiments, four state-of-the-art MOEAs are selected as the comparative algorithms. Experimental results show that the proposed algorithm is superior to the four competitors on both benchmark problems and practical applications, which include the sparse signal reconstruction problem, the community detection problem, and the instance selection problem.

Distributed Heterogeneous Co-Evolutionary Algorithm for Scheduling a Multistage Fine-Manufacturing System With Setup Constraints.

In the context of customization and personalization, the multistage fine-manufacturing system has become emerging in modern manufacturing industries. In this study, the scheduling problem of such a system called distributed hybrid differentiation flowshop scheduling problem is addressed for the first time to minimize makespan. The manufacturing process has three dedicated stages, including distributed fabrication for jobs, assembly from jobs to products, and further differentiation for products to meet customized requirements. Due to the scheduling complexity of the problem, the evolutionary algorithm is designed. First, the population is initialized heuristically and divided into three subpopulations with different identities based on the quality. The identity will be transited dynamically along with evolution. Second, a distributed heterogeneous global exploration strategy is designed to coevolve three subpopulations. According to identity differences, different subpopulations will choose their suitable learning operators and learning strengths to make full use of superior search knowledge. Third, to enhance search intensification, a problem-specific local exploitation strategy consisting of a variable neighborhood local search and a random block local search is devised and carried out adaptively. Fourth, by organizing the global exploration and local exploitation, a novel distributed heterogeneous co-evolutionary algorithm (DHCA) is proposed. The effect of parameter setting on DHCA is investigated by design-of-experiment and computational experiments are carried out to evaluate the algorithm. The results validate the effectiveness of special designs and show that DHCA are more effective and efficient than the compared state-of-the-art algorithms in solving the considered problem.

Automated Prefabricated Slab Splitting Design Using a Multipopulation Coevolutionary Algorithm and BIM

The prefabricated composite slab (PCS) is an essential horizontal component in a building, which is made of a precast part and a cast-in-place concrete layer. In practice, the floor should be split into many small PCSs for the convenience of manufacturing and installation. Currently, the splitting design of PCS mostly relies on sound knowledge and valuable experience of construction. While rule-based parametric design tools using building information modeling (BIM) can facilitate PCS splitting, the generated solution is suboptimal and limited. This paper presents an intelligent BIM-based framework to automatically complete the splitting design of PCSs. A collaborative optimization model is formulated to minimize the composite costs of manufacturing and installation. Individuals with similar area information are grouped into a subpopulation, and the optimization objective is to minimize the specifications and quantities of PCSs. Through the correlation information within the subpopulation and the shared information among each other, the variable correlation is eliminated to accomplish the task of collaborative optimization. The multipopulation coevolution particle swarm optimization (PSO) algorithm is implemented for the collaborative optimization model to determine the sizes and positions of all PCSs. The proposed framework is applied in the optimized splitting design of PCSs in a standard floor to demonstrate its practicability and efficiency.

Parallel cooperative multiobjective coevolutionary algorithm for constrained multiobjective optimization problems

The existing parallel multiobjective evolutionary computation does not perform well for constrained multiobjective optimization problems with discontinuous Pareto fronts or narrow feasible regions. This study parallelizes the state-of-the-art cooperative multiobjective coevolutionary algorithm and proposes an effective parallel evolutionary algorithm for constrained multiobjective optimization problems that are difficult to optimize. Two parallelization methods are compared: a global parallel model in which solution evaluations are performed in parallel, and a hybrid model that treats the cooperative populations in a distributed manner while performing each solution evaluation in parallel. The first model is a straightforward parallelization, while the second one capitalizes on the characteristics of the coevolutionary framework. To investigate the efficacy of the proposed models, experiments are conducted on constrained multiobjective optimization problems, including complex characteristics, while varying the number of parallel cores up to 64. The experiments compare the two proposed methods from the viewpoint of search performance and execution time. The experimental results reveal that the latter hybrid model shows better computational efficiency and scalability against an increasing number of cores without adversely affecting the search performance compared to the former straightforward parallelization.

Double-layer state of health equalization based on cooperative coevolution for large-scale lithium battery system

Due to the fast development of electric vehicles, the rational utilization of retired power lithium batteries is an urgent problem needs to be solved. In order to make better use of the batteries, a preferable equalization method is required. However, the existing studies mainly focus on the equalization circuits and the simplification of control models. This paper further proposes a double-layer state of health (SOH) equalization method namely AMDL-equalization, in which a novel cooperatively coevolving algorithm (AMCCPSO-CG) is developed and the model predictive control (MPC) is also introduced. In the developed AMDL-equalization method, the first equalization layer is used to solve the optimal dispatching problem among battery packs, and the degradation rates of the battery packs are also efficiently adjusted in the first layer. In addition, considering the number of packs in a large-scale battery system, the category-based variable grouping mechanism is proposed and a novel co-evolutionary algorithm namely AMCCPSO-CG is developed to improve the optimal power dispatching performance. For the second equalization layer, the MPC-AMCCPSO-CG algorithm is developed to efficiently optimize the depth of discharge (DOD) and adjust the degradation rate for each battery. Experimental results show that the developed AMDL-equalization method can obtain promising performance on reducing the differences among batteries. The developed method can be applied in the optimal operation control of large-scale energy storage systems, in order to satisfy the future trend of high operational safety and efficiency for retired battery systems.

A Coevolutionary Algorithm With Detection and Supervision Strategies for Constrained Multiobjective Optimization

A Coevolutionary Algorithm With Detection and Supervision Strategies for Constrained Multiobjective Optimization

A coevolutionary algorithm using multi-operator ensemble for many-objective optimisation problems

A coevolutionary algorithm using multi-operator ensemble for many-objective optimisation problems

Design of Amorphous Alloy Composition and Optimization of Vacuum Die Casting Process Parameters Based on Co-evolutionary Algorithm

Design of Amorphous Alloy Composition and Optimization of Vacuum Die Casting Process Parameters Based on Co-evolutionary Algorithm

A Knowledge-Driven Cooperative Coevolutionary Algorithm for Integrated Distributed Production and Transportation Scheduling Problem

A Knowledge-Driven Cooperative Coevolutionary Algorithm for Integrated Distributed Production and Transportation Scheduling Problem

Fuzzy Superposition Operation and Knowledge-driven Co-evolutionary Algorithm for Integrated Production Scheduling and Vehicle Routing Problem with Soft Time Windows and Fuzzy Travel Times

Fuzzy Superposition Operation and Knowledge-driven Co-evolutionary Algorithm for Integrated Production Scheduling and Vehicle Routing Problem with Soft Time Windows and Fuzzy Travel Times

Multi-Agent Q-Net Enhanced Coevolutionary Algorithm for Resource Allocation in Emergency Human-Machine Fusion UAV-MEC System

Multi-Agent Q-Net Enhanced Coevolutionary Algorithm for Resource Allocation in Emergency Human-Machine Fusion UAV-MEC System

A Scalable Parallel Coevolutionary Algorithm With Overlapping Cooperation for Large-Scale Network-Based Combinatorial Optimization

A Scalable Parallel Coevolutionary Algorithm With Overlapping Cooperation for Large-Scale Network-Based Combinatorial Optimization