Co-evolutionary Optimization Research Articles

A Distributed Co-Evolutionary Optimization Method With Motif for Large-Scale IoT Robustness

A Distributed Co-Evolutionary Optimization Method With Motif for Large-Scale IoT Robustness

A comprehensive framework for stochastic calibration and sensitivity analysis of large-scale groundwater models

Abstract. We introduce a comprehensive and robust theoretical framework and operational workflow that can be employed to enhance our understanding, modeling and management capability of complex heterogeneous large-scale groundwater systems. Our framework encapsulates key components such as the three-dimensional nature of groundwater flows, river–aquifer interactions, probabilistic reconstruction of three-dimensional spatial distributions of geomaterials and associated properties across the subsurface, multi-objective optimization for model parameter estimation through stochastic calibration, and informed global sensitivity analysis (GSA). By integrating these components, we effectively consider the inherent uncertainty associated with subsurface system characterizations as well as their interactions with surface waterbodies. The approach enables us to identify parameters impacting diverse system responses. By employing a coevolutionary optimization algorithm, we ensure efficient model parameterization, facilitating simultaneous and informed optimization of the defined objective functions. Additionally, estimation of parameter uncertainty naturally leads to quantification of uncertainty in system responses. The methodology is designed to increase our knowledge of the dynamics of large-scale groundwater systems. It also has the potential to guide future data acquisition campaigns through an informed global sensitivity analysis. We demonstrate the effectiveness of our proposed methodology by applying it to the largest groundwater system in Italy. We address the challenges posed by the characterization of the heterogeneous spatial distribution of subsurface attributes across large-scale three-dimensional domains upon incorporating a recent probabilistic hydrogeological reconstruction specific to the study case. The system considered faces multiple challenges, including groundwater contamination, seawater intrusion, and water scarcity. Our study offers a promising modeling strategy applicable to large-scale subsurface systems and valuable insights into groundwater flow patterns that can then inform effective system management.

Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm

The rapid growth of Electric Vehicles (EVs) has led to issues such as insufficient and unevenly distributed charging stations, posing an increasingly severe challenge. This not only means higher economic and time costs for EV users traveling to charging stations, but also indicates that EVs need to incur additional energy consumption. To address these challenges, this paper first establishes an extended space-time-state network to describe the temporal variability and uncertainty of the traffic environment. Secondly, considering the impact of the energy consumption incurred by EVs traveling to charging stations (CSs) on the layout of CSs, a co-evolutionary optimization model of the location-routing problem is proposed. To solve the model, a two-stage Adaptive Co-evolutionary Clustering Algorithm (ACECA) integrating an adaptive clustering framework and a co-evolutionary mechanism is designed. Finally, the experiment results indicate that there exists a game between a short-term economic investment and long-term benefits of energy conservation and emission reduction. Moreover, ACECA demonstrates stronger solution performance and robustness compared to other algorithms, with the resulting CS location schemes showing superior advantages in energy conservation and in cost saving for users. The research findings can provide theoretical support for the planning of charging station locations for electric vehicles.

An adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection

The feature subset obtained by traditional feature selection algorithms usually contains many irrelevant features and redundant features, which increases the size of the feature set and reduces classification accuracy. More importantly, these irrelevant features and redundant features may lead to local optimum. How to efficiently select the global optimal feature subset from high-dimensional data is a challenging task. To solve the problem, an adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection (ADSCO) is proposed in this paper. The contributions of this algorithm are as follows. Firstly, an adaptive dual strategy constrained optimization mechanism (ADSC) is developed. In the ADSC, the adaptive linear constraint strategy and the random constraint strategy are designed to search for the feature subsets that satisfy the constraints. This can reduce the search space of candidate solutions. Secondly, the dual population-constrained coevolutionary optimizer (CO) is developed to help the algorithm jump out of the local optimum and search for better optimal solutions to further improve the classification accuracy of ADSCO. Finally, the ablation experiments are executed to validate the effectiveness of the proposed ADSC and CO. The ADSCO algorithm is tested on 8 high-dimensional datasets and 8 low-dimensional datasets, and compared with seven typical population feature selection algorithms. Experimental results show that the proposed ADSCO algorithm is superior to the 7 comparison algorithms in terms of classification accuracy and feature subset size, demonstrating its strong competitiveness in tackling high-dimensional data feature selection.

Research on Dynamic Target Search for Multi-UAV Based on Cooperative Coevolution Motion-Encoded Particle Swarm Optimization

Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms.

A cluster-based co-evolutionary optimization method for bilevel multi-objective optimization

The research motivation of multi-objective bilevel optimization mainly stems from the need to solve practical problems and improve decision-making efficiency. On the one hand, bilevel optimization helps to solve the complexity and uncertainty in real life, thereby improving decision-making efficiency and robustness. On the other hand, by promoting the development and application of AI technology, bilevel optimization also provides support for sustainable development. Although the application of bilevel optimization has proven to be beneficial in addressing various real-life problems. However, recent studies indicate that achieving both high speed and high-quality optimization through existing algorithms remains challenging. This difficulty arises due to the NP-hard nature of the bilevel optimization problem. The nested structure method, commonly used to tackle this problem, involves each upper level solution independently performing the lower level optimization task. This approach significantly increases the number of evaluations for the lower level. To address this issue, our proposed method leverages the similarity in lower level optimization to group upper level solutions, enabling co-evolution of lower level solutions within the same group. Consequently, this approach substantially reduces the number of evaluations required for lower level solutions. Additionally, our method pairs parents and offspring, the optimized lower level solutions of the parents are utilized to optimize the lower level solutions of the offspring. This approach accelerates the optimization process for the lower level. To validate the effectiveness of our algorithm, we have applied it to a suite of test problems, demonstrating satisfactory performance.

Obfuscating Community Structure in Complex Network With Evolutionary Divide-and-Conquer Strategy

As the number of social network users grows exponentially with increasingly complex profiles, community detection algorithms play a critical role in user portrait analysis. The associated privacy concerns, however, have not sufficiently received the attention that it deserves. In this work, we investigate methods for obfuscating the original community structure by modifying a small number of connections imperceptibly so as to protect the privacy of users. The existing evolutionary models have some successes in this type of NP-hard problem but can only be applied to small-scale datasets, rendering them inadequate for real-world applications. To alleviate this problem, we propose an original and novel CoeCo, a cooperative evolutionary community obfuscation model. In CoeCo, we leverage the divide-and-conquer strategy and put forward a co-evolutionary optimization algorithm suitable for community structure, in which two different fitness functions promote each other to find the optimal edge set. In addition, the motif hypergraph and permanence are used to improve population initialization. The experimental results indicate that our proposed method can achieve excellent efficacy in obfuscating community structure and also greatly reduces running time.

Enhanced auxiliary population search for diversity improvement of constrained multiobjective coevolutionary optimization

The challenge of solving constrained multiobjective optimization problems (CMOPs) is to obtain solution sets that satisfy all constraints and have global optimal convergence and diversity. However, maintaining the distribution of solutions in small and discrete feasible regions while balancing convergence and feasibility has become increasingly challenging when optimizing many complex CMOPs. To address this issue, this paper proposes an enhanced auxiliary population search, EAPS, based on coevolutionary optimization to improve the distribution of the main population in solving complex CMOPs. Specifically, the auxiliary population adopts a new aggregation method under the decomposition framework to evolve independently without considering any constraints and provides favorable diversity information uni-directionally to the main population. The proposed algorithm is compared with five state-of-the-art constrained multiobjective algorithms on four well-known constraint test suites. The experimental results demonstrate that the proposed are competitive or comparable with the comparison algorithms.

Multi-objective trajectory planning in the multiple strata drilling process:A bi-directional constrained co-evolutionary optimizer with Pareto front learning

Trajectory planning is essential to ensure the safety and efficiency of the drilling process within varying downhole geological conditions. However, numerous constraints and dispersed objectives make this task challenging, resulting in a broken constrained Pareto front (PF) in the objective space. To address this issue, we present a novel multi-objective optimization algorithm (MOEA), the Bi-directional Constrained Co-evolutionary Optimizer (BCCO), for reducing the friction torque of the drilling string system and maintaining the wellbore stability of the directional drilling process in multiple changeable strata. Inspired by dual-population co-evolutionary mechanics, BCCO is thoughtfully developed to enhance complex constraints handling. An ESOINN is introduced to guide the population in exploring the irregular PF and adaptively approximating to it. In addition, a flexible constraint dominance principle is devised to filter out non-inferior infeasible solutions that fully balance feasibility, diversity, and convergence by combining constraint violation, minimum vector angle, and angle-penalized distance. Moreover, we adopt a minimum Manhattan distance (MMD) decision-making method to select the final reference solution for trajectory tracking control. To verify the effectiveness of BCCO, we compared it with seven state-of-the-art constrained MOEAs, four latest published MOEAs used in drilling trajectory planning (DTP) applications, and three ablation MOEAs revised from original BCCO on real-world constrained test instances and four different types of designed standard benchmarks. Extensive testing confirmed adaptability to diverse constrained PFs. BCCO displayed strong convergence and diversity when applied to real-world engineering problems while maintaining feasibility. To evaluate BCCO’s DTP performance, we built a wellbore stability model on actual geological formations and empirically tested it with both composite natural curves and third-order Bézier curves in various drilling trajectory scenarios. We established a comprehensive cloud-based DTP test system with the on-site Programmable Logic Controller (PLC) and the workstation via Data Transfer Unit (DTU) communication. Actual DTP cases confirmed the feasibility of BCCO’s Pareto solutions, with MMD-selected trajectories ensuring a harmonized drilling process.

Co-Evolutionary Algorithm-Based Multi-Unmanned Aerial Vehicle Cooperative Path Planning

Multi-UAV cooperative path planning is a key technology to carry out multi-UAV tasks, and its research has important practical significance. A multi-UAV cooperative path is a combination of single-UAV paths, so the idea of problem decomposition is effective to deal with multi-UAV cooperative path planning. With this analysis, a multi-UAV cooperative path planning algorithm based on co-evolution optimization was proposed in this paper. Firstly, by analyzing the meaning of multi-UAV cooperative flight, the optimization model of multi-UAV cooperative path planning was given. Secondly, we designed the cost function of multiple UAVs with the penalty function method to deal with multiple constraints and designed two information-sharing strategies to deal with the combination path search between multiple UAVs. The two information-sharing strategies were called the optimal individual selection strategy and the mixed selection strategy. The new cooperative path planning algorithm was presented by combining the above designation and co-evolution algorithm. Finally, the proposed algorithm is applied to a rendezvous task in complex environments and compared with two evolutionary algorithms. The experimental results show that the proposed algorithm can effectively cope with the multi-UAV cooperative path planning problem in complex environments.

An Ensemble Surrogate-Based Coevolutionary Algorithm for Solving Large-Scale Expensive Optimization Problems.

Surrogate-assisted evolutionary algorithms (SAEAs) have shown promising performance for solving expensive optimization problems (EOPs) whose true evaluations are computationally or physically expensive. However, most existing SAEAs only focus on the problems with low dimensionality and they rarely consider solving large-scale EOPs (LSEOPs). To fill this research gap, this article proposes an ensemble surrogate-based coevolutionary optimizer for tackling LSEOPs. First, some local surrogate models are trained with low-dimensional data subsets by using feature selection on the large-scale decision variables, a part of which are used to build a selective ensemble surrogate for better approximating the target LSEOP. Then, a coevolutionary optimizer guided by the ensemble surrogate is designed by running two populations to cooperatively solve the target LSEOP and the simplified auxiliary problem. The information of offspring from the two populations is shared to facilitate the coevolution process, which can exploit the searching experience from the simplified auxiliary problem to help solving the target LSEOP. Finally, an effective infill selection criterion is used to update the ensemble surrogate and enhance its approximate performance. To evaluate the performance of the proposed algorithm, a number of well-known benchmark problems are used and the experimental results validate our superior performance over nine state-of-the-art SAEAs on most cases.

Bayesian Co-evolutionary Optimization based entropy search for high-dimensional many-objective optimization

Bayesian evolutionary optimization algorithms have been widely employed to solve expensive many-objective optimization problems. However, the existing approaches are generally designed for low-dimensional problems. In high-dimensional problems, the accuracy of the prediction decreases. And the acquisition function becomes ineffective. The combination of these challenges renders existing approaches unsuitable for selecting potential individual solutions for high-dimensional many-objective optimization problems. To address these limitations, we propose a novel Entropy Search-based Bayesian Co-Evolutionary Optimization approach (ESB-CEO). With the co-evolutionary algorithm as the basic optimizer, it executes an adaptive acquisition function combining the Lp-norm and information entropy to efficiently solve computationally expensive many-objective optimization problems. Individual solutions that have a significant effect on different search stages can be effectively identified, which improves the convergence and diversity of the algorithm. Extensive experimental results based on a set of expensive multi/many-objective test problems demonstrate that the proposed approach significantly outperforms five state-of-the-art surrogate-assisted evolutionary algorithms.

Network planning of metro-based underground logistics system against mixed uncertainties: A multi-objective cooperative co-evolutionary optimization approach

Featured with zero-carbon, jam-free, and high-capacity, the utilization of metro systems for collaborative passenger-and-freight transport (i.e., the metro-based underground logistics system, M-ULS) has been recognized as a favorable alternative to realize automated goods movement in future megacities. This paper proposes an equilibrium chance-constrained programming (ECP) approach for the M-ULS network planning problem (MULNP) considering uncertain demand and costs. Based on the ongoing satellite city development project, the layout formation, facility components, and transportation workflows of a two-tier M-ULS network are designed. The joint decisions (i.e., location, allocation, capacity, inventory, spoke pipeline layout, and flow routing) are formulated as an ECP model with conjoint objectives of minimal fixed cost, operating cost, and penalty cost due to low operating load, wherein the chance-based constraints are further reformulated into crisp equivalent form. To yield high-quality solutions for MULNP problem, an improved multi-objective cooperative co-evolutionary algorithm (MoCC) incorporating non-dominated sorting and chromosomal recombination strategy is proposed and then validated via two groups of instances. Results from the Beijing case study suggest that the proposed method has computational advantages over MoPSO and NSGA-II in acquiring Pareto fronts of large-scale MULNP. The best configurations and layout schemes of the M-ULS networks in both uncertain and deterministic scenarios are analyzed. Based on the findings, several insights for real-world M-ULS network decision-making are provided.

Collaborative Mappers based on Co-evolutionary Optimization Technique in MapReduce

Collaborative Mappers based on Co-evolutionary Optimization Technique in MapReduce

Multi-Objective Optimization Using Cooperative Garden Balsam Optimization with Multiple Populations

Traditional multi-objective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multi-objective optimization problems (MOPs). In this paper, the hybridization of garden balsam optimization (GBO) is presented to solve multi-objective optimization, applying multiple populations for multiple objectives individually. Moreover, in order to improve the diversity of the solutions, both crowding distance computations and epsilon dominance relations are adopted when updating the archive. Furthermore, an efficient selection procedure called co-evolutionary multi-swarm garden balsam optimization (CMGBO) is proposed to ensure the convergence of well-diversified Pareto regions. The performance of the used algorithm is validated on 12 test functions. The algorithm is employed to solve four real-world problems in engineering. The achieved consequences corroborate the advantage of the proposed algorithm with regard to convergence and diversity.

Coevolutionary Framework-Based Constrained Multi-Objective Optimization for Optimal Carbon-Energy Combined Flow of a Power Grid

A new constrained multi-objective optimization coevolutionary algorithm (CCMO) based on the NSGA-II algorithm is proposed to cope with the efficient optimization of multiple objectives containing constraints in the optimal combined carbon-energy flow (OCECF). The algorithm improves the convergence of the population by evolving a new auxiliary population that shares effective information with the original population for weak cooperation, offering significant performance advantages. Applying the algorithm for reactive power control on two different-sized IEEE benchmark systems (IEEE-57 and IEEE-300 bus systems), respectively, minimizes carbon emissions and voltage deviations in the grid. Simulation results show that the CCMO algorithm has significant advantages in terms of the convergence speed and Pareto front.

A*-Based Co-Evolutionary Approach for Multi-Robot Path Planning with Collision Avoidance

In this research, a coevolutionary collision free multi-robot path planning that makes use of A* is proposed. To find collision-free paths for all robots, we generate a route for each of robot using A* path finding but introducing restrictions for each collision found. Afterward, a co-evolutionary optimization process is implemented for introducing changes in the initial paths to find a combination of routes that is collision-free. The approach has been tested in mazes with increasing the number of robots, showing a robust performance although at high time expenses. Nevertheless, several enhancements are proposed to tackle this issue.

Network Planning of Metro-Based Urban Underground Logistics System Against Mixed Uncertainties: A Multi-Objective Cooperative Co-Evolutionary Optimization Approach

Network Planning of Metro-Based Urban Underground Logistics System Against Mixed Uncertainties: A Multi-Objective Cooperative Co-Evolutionary Optimization Approach

The Influence of Introducing Autonomous Vehicles on Conventional Transport Modes and Travel Time

Introducing autonomous vehicles (AVs) on the market is likely to bring changes in the mobility of travelers. In this work, extensive research is conducted to study the impact of different levels of automation on the mobility of people, and full driving automation needs further study because it is still under development. The impacts of AVs on travel behavior can be studied by integrating AVs into activity-based models. The contribution of this study is the estimation of AVs’ impacts on travelers’ mobility when different travel demands are provided, and also the estimation of AVs’ impact on the modal share considering the different willingness of pay to travel by AVs. This study analyses the potential impacts of AVs on travel behavior by investigating a sample of 8500 travelers who recorded their daily activity plans in Budapest, Hungary. Three scenarios are derived to study travel behavior and to find the impacts of the AVs on the conventional transport modes. The scenarios include (1) a simulation of the existing condition, (2) a simulation of AVs as a full replacement for conventional transport modes, and (3) a simulation of the AVs with conventional transport modes concerning different marginal utilities of travel time in AVs. The simulations are done by using the Multi-Agent Transport Simulation (MATSim) open-source software, which applies a co-evolutionary optimization algorithm. Using the scenarios in the study, we develop a base model, determine the required fleet size of AVs needed to fulfill the demand of the different groups of travelers, and predict the new modal shares of the transport modes when AVs appear on the market. The results demonstrate that the travelers are exposed to a reduction in travel time once conventional transport modes are replaced by AVs. The impact of the value of travel time (VOT) on the usage of AVs and the modal share is demonstrated. The decrease in the VOT of AVs increases the usage of AVs, and it particularly decreases the usage of cars even more than other transport modes. AVs strongly affect the public transport when the VOT of AVs gets close to the VOT of public transport. Finally, the result shows that 1 AV can replace 7.85 conventional vehicles with acceptable waiting time.

Dynamic multi-objective cooperative coevolutionary scheduling for mobile underwater wireless sensor networks

Mobile underwater wireless sensor networks (MUWSNs) play an important role in monitoring the marine environment. Many existing research works have engaged on the deployment optimization. Typical examples include rebuilding the network by moving sensors or healing coverage holes using mobile sensors. However, they didn’t make full use of dynamic characteristics of mobile sensors. This paper focuses on the scheduling problem of MUWSNs that consist of a small number of mobile sensors and a large number of fixed ones. To solve the problem, we take mobile sensors as cluster heads to formulate the problem as a multi-objective dynamic scheduling model of fixed sensors as mobile ones move. Then, a dynamic multi-objective cooperative coevolutionary optimization algorithm is developed through decomposing decision variables, designing a strategy to respond environmental changes, and a collaborative strategy for generating an approximate Pareto-optimal set. The feasibility of the proposed optimization model and the effectiveness of its solving algorithm is empirically demonstrated.