Co-evolutionary Strategy Research Articles

Multi-guided population co-evolutionary algorithm based on multiple similarity decomposition for large-scale flexible job shop scheduling problem

As manufacturing shifts towards large-scale production, the size of the workshop increases, and its search space exponentially expands. It is difficult for existing algorithms to obtain an ideal scheduling solution in an acceptable time. For the large-scale flexible job shop scheduling problem (LSFJSP), a multi-guided population co-evolutionary algorithm based on multiple similarity decomposition (MPCSD) is designed. Faced with the problem of high dimensionality and complex solution space, a multiple similarity decomposition strategy is proposed. It proceeds to group based on similarity information at the dimension and population level. To obtain convergence-preferred and diversity-preferred dimension groupings, a training-set solution selection method is proposed. Inspired by the idea of divide-and-conquer, a multi-guided co-evolutionary strategy is proposed. It improves the exploration efficiency of the algorithm in the search space. To test effectiveness on more complex LSFJSP, a set of large-scale test problems including LS1-12 are designed. On LS1-12, MPCSD is compared with seven other algorithms to demonstrate its superiority. MPCSD performed best on 11, 7, and 10 of the 12 test problems on Inverted Generational Distance (IGD), Hypervolume (HV) and Schott’s Spacing Metric (SP), respectively. Meanwhile, the Relative Deviation (RD) results showed that MPCSD obtained the best fitness performance.

Multi-objective multi-population simplified swarm optimization for container loading optimization with practical constraints

The container loading problem (CLP) holds significant importance in logistics and supply chain management due to its direct influence on transportation costs and times, thereby impacting the competitive advantages of enterprises across industries. Although there are existing studies for CLP, there remains a gap in addressing the optimization of multiple objectives while satisfying practical constraints. Moreover, container loading methods must also meet performance requirements such as high computational speed, explainability, and implementation capability. Although meta-heuristic-based algorithms have shown effective computational capabilities and performance, such algorithms often being trapped in the local optima especially when searching in the vast solution space of the CLP, particularly as the quantity and diversity of cargo sizes and shapes increase. Motivated by realistic needs, this study aims to develop a UNISON-based framework that integrates the merging spaces algorithm, a novel simple but effective hybrid simplified swarm optimization genetic algorithm (SSO-GA), and a multi-populations co-evolution strategy to determine the loading sequence of parcels to maximize space utilization and weight balance while satisfying to practical constraints. Specifically, the merging spaces algorithm merges fragmented small spaces resulting from loaded parcels into larger spaces, thereby facilitating the accommodation of additional parcels. The hybrid SSO-GA splits encoded solutions and updates segment using novel strategies resulting in the rearrangement of a group of parcels and their corresponding layouts for better space utilization. Furthermore, the multi-populations co-evolution strategy enhances the diversity of the search space and stabilizes solution quality by simultaneously exploiting the best solutions and exploring alternative solutions during the solution update process. An empirical study was conducted by using a public benchmark dataset which demonstrated the practical effectiveness of the proposed framework by significantly improving average space utilization while ensuring center balance and satisfying practical constraints. Furthermore, this study can also serve as a digital support system capable of assisting decision-makers in optimizing container loading operations, thereby improving productivity and saving valuable time.

Optimized Weights for Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution Strategy

Optimized Weights for Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution Strategy

Short-term industrial load forecasting based on error correction and hybrid ensemble learning

Accurate industrial load forecasting is a prerequisite for ensuring the smooth operation of the power system. Due to the strong fluctuation and complex characteristics of industrial loads, it is difficult to accurately predict short-term power demand. To address this issue, this paper proposes a deep learning prediction model based on hybrid ensemble and error correction. The proposed model is divided into two phases: in the first phase, deep power features are extracted from multivariate data through a hybrid ensemble strategy consisting of Random Subspace, Boosting, Ensemble Pruning, and Multi-Objective Molecular Dynamics Theory Optimization Algorithm (MMDTOA). First, the strategy splits high-dimensional industrial data into multiple sub-datasets. Subsequently, for the features of each sub-dataset, the proposed MMDTOA is applied to perturb the parameters of GRU to generate base learning machines that balance accuracy and diversity. Finally, these base learning machines are integrated by kernel ridge regression stacking. Among them, the two-stage selection strategy and co-evolutionary strategy are embedded into the MMDTOA to enhance the optimization searching effect; in the second stage, a combined error correction strategy is proposed by utilizing the residual information in the prediction results. By combining the dynamic Gaussian error correction function and GRU error correction model, the prediction accuracy of the ensemble model is further improved; Experimental results on real Korean datasets show that the proposed method achieves a minimum value of 3.684% and 7.266% in NMAE and NRMSE, which outperforms eight comparative models, such as SVM, ELM, and CNN, with higher accuracy and robustness.

Constrained multitasking optimization via co-evolution and domain adaptation

Constrained multitasking optimization (CMTO) obtains increasing attention recently. The goal of CMTO is to handle multiple constrained tasks simultaneously. There are two limitations of existing studies on CMTO: (i) existing knowledge transfer techniques for CMTO may not work due to non-intersecting feasible domains and the unconsidered relationship between constraints and objectives; and (ii) knowledge diversity is lacking in existing CMTO algorithms because the representation of knowledge is biased to feasible solutions. To address these limitations, this paper proposes a co-evolution and domain adaptation (CEDA) method for CMTO. First, a new constraint relaxation-based domain adaptation technique for knowledge transfer is devised. Domain adaptation can effectively address the limitations imposed by non-intersecting feasible domains. In addition, as the population evolves, the knowledge representation is biased to different kinds of solutions. Second, a co-evolutionary strategy is proposed to improve the knowledge diversity. The two proposed techniques are with generality and can be readily integrated into different multitasking frameworks. In this paper, the CEDA method is combined with two popular multitasking (i.e., multifactorial-based and multi-population-based) frameworks. The constructed CEDA-based algorithms are compared with fifteen state-of-the-art algorithms on a CMTO benchmark suite and a real-world application. Experimental results demonstrate the superiority of the proposed CEDA method.

Integrating coevolutionary strategies and risk preferences: a novel supervision insight for pollutant abatement

The importance of pollutant abatement has been steadily growing in recent times, prompting an increased focus on developing effective regulatory mechanisms. This paper introduces a novel approach by combining theories of evolutionary games and opinion dynamics to formulate a coevolution model of game and preference. Recognizing the challenges posed by limited supervision ability and enterprises’ heterogeneous risk preferences, we propose a smart supervision mechanism. This mechanism incorporates the concepts of whitelist capability and observation period to establish intelligent supervision. Simulation results demonstrate the regulator’s ability to accurately discern enterprises’ preferences based on decision-making differences. The smart supervision mechanism proves to be more effective in achieving pollutant abatement goals compared to random supervision. Furthermore, our findings indicate that with higher supervision ability, increasing whitelist capability enhances cooperation rates. Conversely, lower supervision ability necessitates a shorter observation period and increased whitelist capability to achieve optimal pollutant abatement results. The study highlights that enterprises with a high cooperation rate experience more significant benefits, while risk-seeking enterprises benefit less due to heightened regulator attention at the same cooperation rate.

Patterns of co-evolution: analyzing fashion brand and sewing contractor company dynamics

The purpose of this study is to identify the entry conditions that could form a co-evolutionary relationship between a sewing contractor company and a fashion brand, and reveal the outcome of such a relationship. In the Republic of Korea, many fashion brands and sewing companies coexist, and the situation in the fashion industry changes with the rapid development of the country, so it is a suitable environment for investigating their co-evolution strategies. A qualitative research methodology was used to examine the co-evolution process of the fashion brand and the sewing industry. In addition, an objective evaluation was conducted by using interviews with sewing contractor companies and fashion brands that have business relationships with the sewing contractor companies. The results indicated that sewing contractor companies evolve into either mass production or small production systems. In the co-evolutionary process, relational resources and business-to-business transaction suitability are the main entry conditions for sewing contractor companies to coevolve with fashion brands. Knowledge exchange, experience optimization, off-season ordering, and investment in trading companies were observed among sewing contractor companies that formed a co-evolutionary relationship with their fashion brand partners. This study identified and conceptualized factors that played a major role in the process of co-evolution and proved suitable for each production system of sewing contractor companies.

A double auxiliary optimization constrained multi-objective evolutionary algorithm

In evolutionary constrained multi-objective optimization, the use of auxiliary optimization is gradually attracting attention. It is noted that different forms of auxiliary optimization have different advantages. Combining these advantages in an appropriate manner can further improve the algorithm’s performance. Motivated by this inspiration, we propose a double auxiliary optimization constrained multi-objective evolutionary algorithm, namely DAO. In DAO, two auxiliary optimizations, i.e., the unconstrained optimization and the (M+1)-objective optimization, are applied in a proposed tri-population co-evolution strategy. In this strategy, three populations are used to optimize the core optimization and the two auxiliary optimizations in an interactive evolution form. Furthermore, DAO develops a (M+1)-objective environmental selection strategy to deeply explore the boundary between the feasible and infeasible regions. In experimental studies, the performance of DAO and four other state-of-the-art algorithms is evaluated on three distinct benchmark test suites and two intricate real-world problem scenarios. The outcome of the comprehensive evaluation shows the competitive of DAO solving constrained multi-objective optimization problems.

An Optimal Model and Application of Hydraulic Structure Regulation to Improve Water Quality in Plain River Networks

The proper dispatching of hydraulic structures in water diversion projects is a desirable way to maximize project benefits. This study aims to provide a reliable, optimal scheduling model for hydraulic engineering to improve the regional water environment. We proposed an improved gravitational search algorithm (IPSOGSA) based on multi-strategy hybrid technology to solve this practical problem. The opposition-based learning strategy, elite mutation strategy, local search strategy, and co-evolution strategies were employed to balance the exploration and exploitation of the algorithm through the adaptive evolution of the elite group. Compared with several other algorithms, the preponderance of the proposed algorithm in single-objective optimization problems was demonstrated. We combined the water quality mechanism model, an artificial neural network (ANN), and the proposed algorithm to establish the optimal scheduling model for hydraulic structures. The backpropagation neural network (IGSA-BPNN) trained by the improved algorithm has a high accuracy, with a coefficient of determination (R2) over 0.95. Compared to the two traditional algorithms, the IGSA-BPNN model was, respectively, improved by 1.5% and 0.9% on R2 in the train dataset, and 1.1% and 1.5% in the test dataset. The optimal scheduling model for hydraulic structures led to a reduction of 46~69% in total power consumption while achieving the water quality objectives. With the lowest cost scheme in practice, the proposed intelligent scheduling model is recommended for water diversion projects in plain river networks.

Multi-Scale Ensemble Booster for Improving Existing TSD Classifiers

Time Series Classification (TSC) is an essential task in Time Series Data (TSD) analysis. Ensemble-based approaches now achieve the best performance on TSC tasks. However, integrating numerous different models makes them highly suffer from heavy preprocessing. Even worse, non-deep-learning ensemble-based methods suffer from substantial computational costs due to lacking GPU acceleration. Multi-scale information in TSD can improve TSC performance. However, Existing TSD classifiers employing multi-scale information struggle with heavy preprocessing and cannot help other TSD classifiers obtain multi-scale feature extraction capabilities. Inspired by these, we proposed a performance enhancement framework called multi-scale ensemble booster (MEB), helping existing TSD classifiers achieve performance leaps. In MEB, we proposed an easy-to-combine network structure without changing any of their structure and hyperparameters, only needed to set one hyperparameter, consisting of multi-scale transformation and multi-output decision fusion. Then, a probability distribution co-evolution strategy is proposed to attain the optimal label probability distribution. We conducted numerous ablation experiments of MEB on 128 univariate datasets and 29 multivariate datasets and comparative experiments with 11 state-of-the-art methods, which demonstrated the significant performance improvement ability of MEB and the most advanced performance of the model enhanced by MEB, respectively. Furthermore, to figure out why MEB can improve model performance, we provided a chain of interpretability analyses.

Configuration Optimization of a Shell-and-Tube Heat Exchanger with Segmental Baffles Based on Combination of NSGAII and MOPSO Embedded Grouping Cooperative Coevolution Strategy

A design indicators prediction model using the Bell-Delaware method for a shell-and-tube heat exchanger with segmental baffles (STHX-SB) is constructed and validated by experiment. The average errors of heat transfer capacity and tube-side pressure drop are 8.52% and 7.92%, respectively, and the predicted weight is the same as the weight obtained by Solidworks commercial software, which indicates the model’s reliability. Parametric influences of the outside diameter of the heat dissipation tube, clearance between heat dissipation tubes, heat dissipation tube length, and tube bundle bypass flow clearance on heat transfer capacity per tube-side pressure drop and heat transfer capacity per weight are studied, and it indicates that whether the interaction between factors is considered or not, both heat transfer capacity per tube-side pressure drop and heat transfer capacity per weight are the most sensitive to outside diameter of heat dissipation tube and the least sensitive to heat dissipation tube length based on the Sobol’ method. To avoid falling into local optima due to algorithm convergence being too fast and to improve the reliability of solving complex optimization problems, Non-Dominated Sorted Genetic Algorithm II (NSGAII) and Multi-Objective Particle Swarm Optimization (MOPSO) embedded grouping cooperative coevolution (NSGAII-MOPSO-GCC) is proposed to optimize the studied four configuration parameters to maximize heat transfer capacity per tube-side pressure drop and heat transfer capacity per weight for STHX-SB, simultaneously. Compared with the original structure, heat transfer capacity per tube-side pressure drop and heat transfer capacity per weight of the chosen solutions separately increased by 57.66% and 4.63%, averagely, and in the optimization comparison of NSGAII, MOPSO, and NSGAII-MOPSO-GCC, NSGAII-MOPSO-GCC has the best performance, which shows that the proposed method is effective and feasible and can supply beneficial solutions and valuable guidance for heat exchanger design and improvement.

Surrogate-assisted evolutionary optimization for perishable inventory management in multi-echelon distribution systems

Simulation is widely used for analyzing supply chains with complex structures and stochastic nature. However, optimizing supply chain simulation models is usually computationally expensive. This study proposes a surrogate-assisted evolutionary optimization approach to optimize the inventory policies in multi-echelon distribution systems for perishable items under a limited number of evaluations. The random forest algorithm is used to build the surrogate model for a faster estimation of the performance of the inventory policies. A co-evolutionary differential evolution algorithm is proposed to simultaneously evolve the population through multiple searching strategies. The generated solutions are estimated by the low-cost surrogate model to select the promising solutions, which will be evaluated by the inventory model. Moreover, this study also integrates the surrogate model into two classic metaheuristic algorithms, particle swarm optimization and differential evolution. Also, a new performance indicator is proposed to examine the efficiency of the surrogate model in evolutionary computation. Two case studies are used to investigate the performance of the proposed algorithms. The experimental results show that both particle swarm optimization and differential evolution exhibit performance improvements exceeding 55% by using surrogate models under the limited number of function evaluations. Furthermore, the surrogate model reduces computational time for both algorithms by over 34% to achieve equivalent objective values. Finally, the proposed co-evolutionary differential evolution algorithm is compared with 12 algorithms, and the results show that the proposed algorithm consistently outperforms them. These findings confirm the usefulness of the surrogate model in evolutionary algorithms and the effectiveness of the proposed co-evolutionary strategy for solving perishable inventory problems.

A multi-preference-based constrained multi-objective optimization algorithm

When tackling constrained multi-objective optimization problem, evolutionary algorithms grapple with the simultaneous need to optimize the conflict objectives and satisfy constraints. The preference for the algorithm in processing objectives and constraints directly affects the feasibility, diversity and convergence of population. At the same time, the decision regarding algorithmic preference should be based on the characteristics of the current evolution. Therefore, preference decision of algorithm is a intractable challenge throughout the optimization. To address this issue, a multi-preference-based constrained multi-objective optimization algorithm is proposed in this paper, operating under the aegis of three evolutionary models. The preferences are determined by the analysis of the evolution states in concert with the actual characteristic of the population, and are implemented through the reasonable scheduling evolutionary models. When the preference changes, the algorithm identifies the useful knowledges carried by the previous populations and transfers them to the current populations. In addition, a shift-based update strategy and a new co-evolution strategy are designed for different models in the proposed algorithm, respectively. Compared to five state-of-the-art multi-objective algorithms on two benchmark suits and two real-world applications, the proposed algorithm performs better than its peers, especially in diversity difficulty and convergence difficulty multi-objective optimization problems.

Analysis of power system scheduling operation mode based on multi-objective optimization algorithm

Abstract Exploring the majorization strategy of the power system (PS) dispatching operation is to achieve economic cost reduction and reduce environmental pollution. In this paper, starting from the PS dispatching model, the adaptive Corsi variance is introduced to get rid of the local optimum using particle swarm majorization procedure, and the adaptive Corsi variance multiple swarm coevolutionary procedure is constructed through coevolutionary strategy and information sharing strategy. The MCPSO-ACPM procedure is used to optimize the PS scheduling operation model, and experiments are conducted on both load and unit for the optimized scheduling model. From the load majorization results, the peak-to-valley variance is concentrated from 176.02KW to 110.51KW compared with the original load, and the peak-to-valley ratio is reduced by 0.718, which saves customers 98.63 yuan in electricity purchase cost. From the scheduling majorization prediction, the PS output power prediction value of 1 min during the day is closest to the actual measured value of output power, and its prediction deviation is about 2.67%. This shows that the use of a multi-objective majorization procedure can realize the optimal dispatch of PS and achieve the reduction of economic cost.

Synthesis of Large Ultrawideband Sparse Planar Arrays by Utilizing a Cooperative Coevolutionary Iterative Fourier Transform

This letter introduces a cooperative coevolutionary iterative Fourier transform (CCIFT) technique to extend the conventional IFT to large ultra-wideband (UWB) sparse planar array synthesis. In the proposed CCIFT, a dense initial element position grid such as a spacing of half a wavelength at the highest frequency is adopted to avoid the presence of grating lobes at high frequency band, and a probability-assisted element selection mechanism is presented to guarantee the obtained array layout to meet a preset minimum spacing constraint, enabling the placement of physical UWB antenna elements. In addition, a cooperative coevolutionary strategy is presented to avoid prematurity by establishing the information interaction between different excitation distributions from multiple parallel IFT procedures. Two examples are conducted and results show that the CCIFT can achieve a considerably lower peak sidelobe level (PSL) than some state-of-art algorithms within an acceptable CPU time, which demonstrates its effectiveness and superiority.

A dynamic dual-population co-evolution multi-objective evolutionary algorithm for constrained multi-objective optimization problems

Many multi-objective evolutionary algorithms are proposed to handle constrained multi-objective optimization problems. Nevertheless, they often fail to appropriately balance feasibility, convergence and diversity of the population. This paper proposed a dynamic dual-population co-evolution multi-objective evolutionary algorithm (DDCMEA) to solve this issue. In DDCMEA, a dynamic dual-population co-evolution strategy is employed to balance the convergence and the feasibility by dynamically adjusting the offspring number of the two populations. In the early stage of evolution, the algorithm mainly focuses on the convergence and more offspring of the first population are generated. In the late stage of evolution, the algorithm mainly focuses on the feasibility and more offspring of the second population are generated. Finally, feasible solutions with good convergence could be obtained. To further enhance the diversity of the offspring and obtain feasible solutions with a wide spread of distribution, the evolution operators of the genetic algorithm and the differential evolution are chosen as the search engines for the first population and the second population, respectively. The performance of DDCMEA is further tested through thirty-one bench-mark test problems and two real-world problems in comparison with other five state-of-the-art algorithms. The results show the proposed algorithm DDCMEA achieves competitive performance when handling constrained multi-objective optimization problems.

A Two-stage Multi-population Genetic Algorithm with Heuristics for Workflow Scheduling in Heterogeneous Distributed Computing Environments

Workflow scheduling in Heterogeneous Distributed Computing Environments (HDCEs) is a NP-hard problem. Although a number of scheduling approaches have been proposed for workflow scheduling in HDCEs, there is still a room and need for improvement. To fill the gaps, this study formulates workflow scheduling problem in HDCEs as a complete, solvable and extensible integer programming mathematical model with precedence and resource constraints that provides a theoretical foundation for developing workflow scheduling strategy. Then, this study develops a novel two-stage multi-population genetic algorithm with heuristics for workflow scheduling. In particular, two-stage multi-population coevolution strategy is employed with designed novel methods for population initialization, genetic operation, individual decoding and improvement. To estimate the validity, extensive experiments are designed and conducted on various scenarios based on real and random workflow applications. The results have shown the practical viability of the proposed algorithm outperforming conventional approaches.

Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

Coevolutionary strategies at the collective level for improved generalism

Abstract In many complex practical optimization cases, the dominant characteristics of the problem are often not known prior. Therefore, there is a need to develop general solvers as it is not always possible to tailor a specialized approach to each application. The previously developed multilevel selection genetic algorithm (MLSGA) already shows good performance on a range of problems due to its diversity-first approach, which is rare among evolutionary algorithms. To increase the generality of its performance, this paper proposes utilization of multiple distinct evolutionary strategies simultaneously, similarly to algorithm selection, but with coevolutionary mechanisms between the subpopulations. This distinctive approach to coevolution provides less regular communication between subpopulations with competition between collectives rather than individuals. This encourages the collectives to act more independently creating a unique subregional search, leading to the development of coevolutionary MLSGA (cMLSGA). To test this methodology, nine genetic algorithms are selected to generate several variants of cMLSGA, which incorporates these approaches at the individual level. The mechanisms are tested on 100 different functions and benchmarked against the 9 state-of-the-art competitors to evaluate the generality of each approach. The results show that the diversity divergence in the principles of working of the selected coevolutionary approaches is more important than their individual performances. The proposed methodology has the most uniform performance on the divergent problem types, from across the tested state of the art, leading to an algorithm more likely to solve complex problems with limited knowledge about the search space, but is outperformed by more specialized solvers on simpler benchmarking studies.

Service scheduling optimization for multiple tower cranes considering the interval time of the cross-tasks.

The key issues that have always affected the production yield of the construction industry are delays and cost overruns, especially when dealing with large-scale projects and super-high buildings in which multiple tower cranes with overlapping areas are often deployed because of urgent due date and limited space. The service scheduling of tower cranes, which act as the crucial site equipment for lifting and transporting materials, is one of the main problems not only related to the construction progress and project cost but also affecting equipment health, and it may bring security risks. The current work presents a multi-objective optimization model for a multiple tower cranes service scheduling problem (MCSSP) with overlapping areas while achieving maximum interval time of cross-tasks and minimum makespan. For the solving procedure, NSGA-Ⅱ is employed with double-layer chromosome coding and simultaneous coevolutionary strategy design, which can obtain a satisfactory solution through effectively allocating tasks within overlapping areas to each crane and then prioritizing all the assigned tasks. The makespan was minimized, and stable operation of tower cranes without collision was achieved by maximizing the cross-tasks interval time. A case study of the megaproject Daxing International Airport in China has been conducted to evaluate the proposed model and algorithm. The computational results illustrated the Pareto front and its non-dominant relationship. The Pareto optimal solution outperforms the results of the single objective classical genetic algorithm in terms of overall performance of makespan and interval time of cross-tasks. It also can be seen that significant improvement in the time interval of cross-tasks can be achieved at the cost of a tiny increase in makespan, which means effective avoidance of the tower cranes entering the overlapping area at the same time. This can help eliminate collision, interference and frequent start-up and braking of tower cranes, leading to safe, stable and efficient operation on the construction site.