Cooperative Coevolutionary Approach Research Articles

Decision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling

Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.

Adaptive cooperative coevolutionary differential evolution for parallel feature selection in high-dimensional datasets

In many fields, it is a common practice to collect large amounts of data characterized by a high number of features. These datasets are at the core of modern applications of supervised machine learning, where the goal is to create an automatic classifier for newly presented data. However, it is well known that the presence of irrelevant features in a dataset can make the learning phase harder and, most importantly, can lead to suboptimal classifiers. Consequently, it is becoming increasingly important to be able to select the right subset of features. Traditionally, optimization metaheuristics have been used with success in the task of feature selection. However, many of the approaches presented in the literature are not applicable to datasets with thousands of features because of the poor scalability of optimization algorithms. In this article, we address the problem using a cooperative coevolutionary approach based on differential evolution. In the proposed algorithm, parallelized for execution on shared-memory architectures, a suitable strategy for reducing the dimensionality of the search space and adjusting the population size during the optimization results in significant performance improvements. A numerical investigation on some high-dimensional and medium-dimensional datasets shows that, in most cases, the proposed approach can achieve higher classification performance than other state-of-the-art methods.

Offline Data-Driven Optimization at Scale: A Cooperative Coevolutionary Approach

Offline Data-Driven Optimization at Scale: A Cooperative Coevolutionary Approach

A Novel Self-Adaptive Cooperative Coevolution Algorithm for Solving Continuous Large-Scale Global Optimization Problems

Unconstrained continuous large-scale global optimization (LSGO) is still a challenging task for a wide range of modern metaheuristic approaches. A cooperative coevolution approach is a good tool for increasing the performance of an evolutionary algorithm in solving high-dimensional optimization problems. However, the performance of cooperative coevolution approaches for LSGO depends significantly on the problem decomposition, namely, on the number of subcomponents and on how variables are grouped in these subcomponents. Also, the choice of the population size is still an open question for population-based algorithms. This paper discusses a method for selecting the number of subcomponents and the population size during the optimization process (“on fly”) from a predefined pool of parameters. The selection of the parameters is based on their performance in the previous optimization steps. The main goal of the study is the improvement of coevolutionary decomposition-based algorithms for solving LSGO problems. In this paper, we propose a novel self-adapt evolutionary algorithm for solving continuous LSGO problems. We have tested this algorithm on 15 optimization problems from the IEEE LSGO CEC’2013 benchmark suite. The proposed approach, on average, outperforms cooperative coevolution algorithms with a static number of subcomponents and a static number of individuals.

Automatic Design of Efficient Heuristics for Two-Stage Hybrid Flow Shop Scheduling

This paper addresses the two-stage hybrid flow shop scheduling problem with a batch processor in the first stage and a discrete processor in the second stage. Incompatible job families and limited buffer size are considered. This hybrid flow shop configuration commonly appears in manufacturing operations and the batch processor is always the bottleneck which breaks the symmetry of processing time. Since making a real-time high-quality schedule is challenging, we focus on the automatic design of efficient heuristics for this two-stage problem based on the genetic programming method. We develop a hyper-heuristic approach to automate the tedious trial-and-error design process of heuristics. The goal is to generate efficient dispatching rules for identifying complete schedules to minimize the total completion time. A genetic programming with cooperative co-evolution approach is proposed to evolve the schedule policy automatically. Numerical results demonstrate that the proposed approach outperforms both the constructive heuristic and meta-heuristic algorithms, and is capable of producing high-quality schedules within seconds.

Variable Decomposition for Large-Scale Constrained Optimization Problems Using a Grouping Genetic Algorithm

Several real optimization problems are very difficult, and their optimal solutions cannot be found with a traditional method. Moreover, for some of these problems, the large number of decision variables is a major contributing factor to their complexity; they are known as Large-Scale Optimization Problems, and various strategies have been proposed to deal with them. One of the most popular tools is called Cooperative Co-Evolution, which works through a decomposition of the decision variables into smaller subproblems or variables subgroups, which are optimized separately and cooperate to finally create a complete solution of the original problem. This kind of decomposition can be handled as a combinatorial optimization problem where we want to group variables that interact with each other. In this work, we propose a Grouping Genetic Algorithm to optimize the variable decomposition by reducing their interaction. Although the Cooperative Co-Evolution approach is widely used to deal with unconstrained optimization problems, there are few works related to constrained problems. Therefore, our experiments were performed on a test benchmark of 18 constrained functions under 100, 500, and 1000 variables. The results obtained indicate that a Grouping Genetic Algorithm is an appropriate tool to optimize the variable decomposition for Large-Scale Constrained Optimization Problems, outperforming the decomposition obtained by a state-of-the-art genetic algorithm.

An Agent-Based Cooperative Co-Evolutionary Approach for the Many-Objective Optimal Production Planning in Energy Supply Chains

An Agent-Based Cooperative Co-Evolutionary Approach for the Many-Objective Optimal Production Planning in Energy Supply Chains

An Agent-Based Cooperative Co-Evolutionary Approach for the Many-Objective Optimal Production Planning in Energy Supply Chains

An Agent-Based Cooperative Co-Evolutionary Approach for the Many-Objective Optimal Production Planning in Energy Supply Chains

A lexicographic cooperative co-evolutionary approach for feature selection

This paper starts with two hypotheses. The first one is that the simultaneous optimization of the hyperparameters regulating the classifier within a wrapper method, while the best subset of features is being determined, should improve the results with respect to those obtained with a pre-parameterized classifier. The second one is that solving these two problems can be formulated as a lexicographic optimization problem, allowing the use of a simple single-objective evolutionary algorithm to solve this multi-objective problem.The fitness function is of key importance for such wrapper methods. It is responsible for guiding the search towards potentially good solutions and it also consumes most of the runtime. Having these issues in mind, this paper also proposes a new lexicographic fitness function, designed to minimize the runtime of the algorithm and also to avoid over-fitting. Furthermore, the execution time and the quality of the results obtained by the wrapper procedure also depend on some algorithmic hyperparameters: the similarity thresholds used when comparing two different solutions lexicographically and the percentage of data samples used for validation during the training process. Thus, an experimental analysis has been carried out to find adequate values for these hyperparameters. Finally, the lexicographic cooperative co-evolutionary wrapper approach, using the new fitness function proposed in this paper, has been tested with several datasets belonging to the University of California, Irvine (UCI) repository and also with some real high-dimensional datasets, obtaining quite good results, compared to other state-of-the-art wrapper methods. The comparison has also been made lexicographically, with a new methodology proposed in this paper.

Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

Cooperative Coevolution Design of Multilevel Fuzzy Logic Controllers for Media Access Control in Wireless Body Area Networks

Fuzzy logic techniques have been successfully employed in wireless body area networks (WBANs) for media access control (MAC). However, most of the existing research work considered only single-level fuzzy logic controls (FLCs) for either contention-based or contention-free MAC, neglecting the importance of utilizing both contention-based and contention-free MAC to improve the reliability and performance of the network. This paper introduces a two-level FLC control scheme at both the coordinator level and the sensor level to further enhance communication quality in WBANs. The sensor-level FLC controls contention-based channel access and the coordinator-level FLC controls contention-free channel access. This two-level FLC architecture can effectively improve the cooperation between sensors and the coordinator such that both the performance and reliability of the network can be significantly improved. We also proposed a cooperative coevolutionary approach to design of our proposed two-level control scheme automatically. With the goal of effectively designing useful FLCs, we have particularly developed two new collaborator selection methods for our cooperative coevolutionary approach, which enable us to effectively select collaborators while evaluating the candidate FLC design in each subpopulation. Specifically, we demonstrate that, by employing network knowledge, our collaborator selection methods can judge the suitability of potential collaborator designs and notably improve the effectiveness of our evolutionary design approach. Empirically the FLCs designed by our approach clearly outperformed several state-of-the-art MAC schemes from recent literature and the IEEE 802.15.4 standard.

A Cooperative Coevolutionary Approach to Discretization-Based Feature Selection for High-Dimensional Data.

Recent discretization-based feature selection methods show great advantages by introducing the entropy-based cut-points for features to integrate discretization and feature selection into one stage for high-dimensional data. However, current methods usually consider the individual features independently, ignoring the interaction between features with cut-points and those without cut-points, which results in information loss. In this paper, we propose a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO), which searches for the feature subsets with and without entropy-based cut-points simultaneously. For the features with cut-points, a ranking mechanism is used to control the probability of mutation and crossover in GA. In addition, a binary-coded PSO is applied to update the indices of the selected features without cut-points. Experimental results on 10 real datasets verify the effectiveness of our algorithm in classification accuracy compared with several state-of-the-art competitors.

Improved random adaptive grouping approach for solving unconstrained LSGO problems

Large-scale global optimization (LSGO) problems are emergent in many domains of applied sciences. LSGO is a hard challenge for the majority of state-of-the-art optimization methods. Evolution algorithms (EAs) combined with Cooperative Coevolution (CC) are able to perform well when solving many real-world LSGO problems. The grouping of variables at the problem decomposition stage has a significant impact on the performance of the CC approach. This study proposes an improvement of the previously developed random adaptive grouping (RAG) approach for CC. The new method is titled as RAG2, and the whole optimization algorithm, based on RAG2, is called CC-SHADE-RAG2. The influence of the choice of the population size and the number of subcomponents on the algorithm performance have been investigated using the IEEE LSGO CEC’2013 benchmark. The set of test problems in the benchmark contains fifteen functions with dimensionality equal to one thousand. We have also compared the performance of the novel algorithm with some EAs, which are applied for solving LSGO problems.

Feature Selection and Instance Selection from Clinical Datasets Using Co-operative Co-evolution and Classification Using Random Forest

ABSTRACT Co-operative co-evolution approach solves problems by breaking them into subproblems. The proposed framework for Clinical Decision Support System (CDSS) uses a co-operative coevolution approach which treats Feature Selection (FS) and Instance Selection (IS) as independent subproblems. FS and IS remove less relevant features and instances, respectively, thereby improving the overall performance of the system. In this work, both Feature and Instance selection are done using the wrapper approach, which uses co-operative co-evolution and random forest classifier. The reduced dataset is used to train a random forest classifier and this trained model helped in making clinical decisions. These decisions assist physicians as the second opinion for diagnosis and treatment. Wisconsin Diagnostic Breast Cancer (WDBC), Hepatitis, Pima Indian Diabetes (PID), Cleveland Heart Disease (CHD), Statlog Heart Disease (SHD), Vertebral Column, and Hepatocellular Carcinoma (HCC) datasets from the University of California Irvine (UCI) Machine Learning repository are used for experimentation. The proposed framework achieved an accuracy of 97.1%, 82.3%, 81.01%, 93.4%, 96.8%, 91.4%, and 72.2% for datasets WDBC, Hepatitis, PID, CHD, SHD, vertebral column, and HCC, respectively. The results prove that the CDSS-developed using co-operative coevolution can efficiently assist the physicians in decision-making.

Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data

Evolutionary feature selection (FS) methods face the challenge of “curse of dimensionality” when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for FS. The proposed algorithm employs the idea of “divide and conquer” in cooperative coevolutionary approach, but several new developed problem-guided operators/strategies make it more suitable for FS problems. First, a space division strategy based on the feature importance is presented, which can classify relevant features into the same subspace with a low computational cost. Following that, an adaptive adjustment mechanism of subswarm size is developed to maintain an appropriate size for each subswarm, with the purpose of saving computational cost on evaluating particles. Moreover, a particle deletion strategy based on fitness-guided binary clustering, and a particle generation strategy based on feature importance and crossover both are designed to ensure the quality of particles in the subswarms. We apply VS-CCPSO to 12 typical datasets and compare it with six state-of-the-art methods. The experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling FS problems with high dimensionality.

Cooperative co-evolutionary approach to optimize recovery for improving resilience in multi-communities

Communities around the world are showing to be still highly vulnerable to natural and man-made hazards. The increasing complexity and interconnectivity of the systems which make the lifeline of communities, has led to a strong emphasis on communities’ resilience. Recovery after disruptions is a key step for building the resilience of communities. This process is influenced by the allocation of resources from different communities and the choice between investments on infrastructure recovery or local emergency solutions. Therefore, the overall community resilience emerges from the coordinated decision-making among the impacted communities and from the trade-off between global and local investments. This work proposes a methodology for resilient recovery of interdependent infrastructure systems driven by multi-community decision making, sustained by a cooperative co-evolution approach to optimization. The methodology is deployed in a simulation model that constitutes a practical tool for restoration management.

Contribution Based Co-Evolutionary Algorithm for Large-Scale Optimization Problems

The solution of large-scale optimization problems is the key to many decision-making processes in practice. However, it is a challenging research topic when considered both the quality of solutions and the required computational time. One of the popular approaches for these problems is to divide the problems into a number of smaller sub-problems, that are then solved separately with an exchange of some information using the cooperative co-evolution (CC) concept. However, the characteristics of sub-components could be different, and their contributions to the overall performance can also be different while solving the problem. In the CC approach, it usually applies one optimizer and allocates equal computational budget to all sub-components. In this article, a new algorithm is proposed with the use of multiple optimizers, along with a need-based allocation of computational budget for the sub-components. In the proposed algorithm, a group of optimizers cooperate in an effective way to evolve the sub-components, depending on heuristic fuzzy rules. The performance of our proposed algorithm was evaluated by solving a number of large-scale global optimization benchmark functions. The empirical results show that the proposed algorithm outperforms equal allocation CC, a single selection characteristic, a single candidate optimizer and state-of-the-art algorithms.

A Cooperative Co-Evolutionary Approach to Large-Scale Multisource Water Distribution Network Optimization

Potable water distribution networks (WDNs) are important infrastructures of modern cities. A good design of the network can not only reduce the construction expenditure but also provide reliable service. Nowadays, the scale of the WDN of a city grows dramatically along with the city expansion, which brings heavy pressure to its optimal design. In order to solve the large-scale WDN optimization problem, a cooperative co-evolutionary algorithm is proposed in this paper. First, an iterative trace-based decomposition method is specially designed by utilizing the information of water tracing to divide a large-scale network into small subnetworks. Since little domain knowledge is required, the decomposition method has great adaptability to multiform networks. Meanwhile, during optimization, the proposed algorithm can gradually refine the decomposition to make it more accurate. Second, a new fitness function is devised to handle the pressure constraint of the problem. The function transforms the constraint into a part of the objective to punish the infeasible solutions. Finally, a new suite of benchmark networks are created with both balanced and imbalanced cases. Experimental results on a widely used real network and the benchmark networks show that the proposed algorithm is promising.

Scenario-based planning of fast charging stations considering network reconfiguration using cooperative coevolutionary approach

Abstract In recent years, plug-in electric vehicles (PEVs) have attracted a great deal of attention because of environmental issues. For large-scale integration of PEVs, appropriate siting and sizing of PEV charging stations are essential. In this paper, a framework for simultaneous fast charging stations (FCSs) planning and distribution network reconfiguration is proposed. The distribution network reconfiguration is effectively used to optimize objective functions. The objective function considered is defined as the sum of the initial investment and energy loss costs subject to different distribution system constraints. In the model, a scenario-based approach is used for taking into account uncertainties of traditional electric load, FCS load, and electricity price. To reduce the computational burden of the problem, a scenario reduction technique is utilized. A cooperative coevolutionary genetic algorithm (CCGA) is employed to solve the problem. The FCSs locations and the network switches are coded separately, and they evolve cooperatively as two different species, i.e., the location species and the switch species. Furthermore, an efficient technique called Dijkstra's algorithm is used to calculate FCSs sizes in which the nearest FCS to PEVs is determined based on their driving distance from FCSs on their path to their destination. Finally, the performance and the effectiveness of the proposed method are demonstrated by numerical results. More specifically, use of the proposed scenario-based approach yields to a more accurate solution than deterministic method. In addition, usage of network reconfiguration in FCSs planning both reduces the total costs and improves the voltage profile.

Dynamic Team Heterogeneity in Cooperative Coevolutionary Algorithms

We propose Hyb-CCEA, a cooperative coevolutionary algorithm for the evolution of genetically heterogeneous multiagent teams. The proposed approach extends the cooperative coevolution architecture with operators that put the number of coevolving populations under evolutionary control. Populations are dynamically merged based on behavioral similarity, thus decreasing team heterogeneity, and stochastic population splits are used to explore increased team heterogeneity. Hyb-CCEA is capable of converging to suitable team compositions for the given task, be it a completely homogeneous team where all agents share the same control logic, a heterogeneous team where each agent has distinct control logic, or a partially heterogeneous team. By placing both team composition and agent controllers under evolutionary control, Hyb-CCEA can be applied to domains for which the experimenter has limited or no knowledge about possible solutions. We study Hyb-CCEA extensively in an abstract domain, and conduct a series of validation experiments with four simulated multirobot tasks: two multirover foraging tasks and two robotic soccer tasks. The results show that Hyb-CCEA takes advantage of partial heterogeneity and frequently outperforms the standard cooperative coevolution approach, both in terms of fitness scores achieved and number of evaluations needed to evolve solutions.