Co-evolutionary Strategy Research Articles

Data-driven robust optimal control for nonlinear system with uncertain disturbances

Optimal control methods have attracted much attention for their promising performance in nonlinear systems. However, it is difficult to achieve satisfactory performance due to uncertain disturbances. To cope with this problem, a data-driven robust optimal control (DROC) method is proposed for uncertain nonlinear systems. The merits of the proposed DROC method are threefold: First, a data-driven evaluation strategy is introduced to capture the relationship between the approximating errors and the control variables. Then, the control performance indexes of nonlinear systems can be established within uncertain disturbances. Second, a multi-objective robust optimization algorithm is developed with a coevolution strategy. Then, robust optimal control laws can be obtained to improve the control performance. Third, the robust boundedness of DROC is discussed in theory. Then, the stability of the control systems can be guaranteed analytically. Finally, the effectiveness of DROC is illustrated with two multiple input multiple output second-order nonlinear systems. The optimal control performances are displayed in experiments to demonstrate the effectiveness of DROC.

Formation Control with Connectivity Assurance for Missile Swarms by a Natural Co-Evolutionary Strategy

Formation control is one of the most concerning topics within the realm of swarm intelligence. This paper presents a metaheuristic approach that leverages a natural co-evolutionary strategy to solve the formation control problem for a swarm of missiles. The missile swarm is modeled by a second-order system with a heterogeneous reference target, and the exponential of the resultant error is accumulated to be the objective function such that the swarm converges to optimal equilibrium states satisfying specific formation requirements. Focusing on the issue of the local optimum and unstable evolution, we incorporate a novel model-based policy constraint and a population adaptation strategy that significantly alleviates the performance degradation of the existing natural co-evolutionary strategy in terms of slow training and instability of convergence. With application of the Molloy–Reed criterion in the field of network communication, we developed an adaptive topology method that assures connectivity under node failure, and its effectiveness is validated theoretically and experimentally. The experimental results demonstrate that the accuracy of formation flight achieved by this method is competitive with that of conventional control methods and is much more adaptable. More significantly, we show that it is feasible to treat the generic formation control problem as an optimal control problem for finding a Nash equilibrium strategy and solving it through iterative learning.

Optimization of Oil Pipeline Operations to Reduce Energy Consumption Using an Improved Squirrel Search Algorithm

To achieve the goal of achieving carbon-neutral by 2060, the government of China has put forward higher requirements for energy conservation and consumption reduction in the energy industry. Therefore, it is necessary to reduce energy consumption in the process of transporting oil. In this paper, an optimization model that minimizes the total energy consumption of the entire pipeline system is proposed and the squirrel search algorithm (SSA) is used to solve the optimization model. Meanwhile, to improve the performance of the SSA, two strategies are proposed. One is the adaptive inertia weight strategy, and the other is the multi-group co-evolution strategy. The adaptive inertia weight can adjust the step size of the flying squirrels according to the difference of the objective function value and multi-group co-evolution is introduced to improve population diversity. The improved SSA is named multigroup coevolution-adaptive inertia weight SSA (MASSA). A total of 20 benchmark functions are used to test the performance of MASSA, including unimodal functions and multimodal functions. Compared with the other four algorithms, MASSA has better performance and convergence capabilities. In the case study experiment, an optimization model of the oil pipeline is built, which takes the minimum energy consumption of the whole pipeline as the objective function. Compared with the actual operating conditions, the electricity consumption optimized by MASSA decreases by 399,018.94 kgce, and the thermal energy dissipation decreases by 113,759.25 kgce. The total energy consumption is reduced by 512,778.19 kgce, which is 9.62%. These results indicate that the two improvement strategies are significant, and optimizing the operating parameters can reduce energy consumption.

The economy of the Sino–US conflict: its impact on Chinese firms listed in the US and Hong Kong as a financial hub

ABSTRACT The US is currently pursuing a cold war strategy against China, having initiated a trade war and delisted Chinese companies to restrict China’s development. Although China interacts with other countries through foreign direct investment, it prevents the circulation of speculative monies from the international market by enclosing its national capital account. As many Chinese companies have a variable interest entity structure, their delisting from the US will motivate relisting in Hong Kong. The US and China need to conceive a coevolution strategy to generate a better world.

A Multitime Window Parallel Scheduling System for Large-Scale Offshore Platform Project

In order to complete the offshore platform project scheduling intelligently, an improved scheduling optimization system based on the parallel genetic algorithm was proposed. An optimal model for the large-scale offshore platform project scheduling problem (LSOPPSP) was built and produced the mathematic model of LSOPPSP, based on the characteristics of the abundance of activities, long duration, high uncertainty, and frequent changes. In long-term unsteady manufacturing, this model can provide good robustness. In addition, the essential steps of the multitime window parallel genetic algorithm were proposed. An improved population initialization algorithm was designed, as well as the coevolution strategy among populations was also proposed in parallel computing. These two strategies can increase population variety while also speeding up convergence. Finally, the suggested parallel scheduling system was deployed in our self-developed schedule optimization software for offshore platform enterprises, and the outperformance of the improved algorithm was proven by simulated examples and practical application.

A rapid sustainability dynamic assessment of the USA and China 1995-2018.

The USA and China have the world's largest GDP and greenhouse gas emissions, as well as having a significant proportion of the world's total population. These form part of the triumvirate of influencing factors which potentially detrimentally impact upon sustainability. How the triumvirate factors have impacted upon the sustainability of the USA and China has not been adequately determined quantitatively or holistically. Understanding this can strongly indicate how significant levels of population, economic growth, and greenhouse gas emissions impacts and influences the indicated levels, nature, and dynamics of sustainability occurring. Therefore, to contribute towards developing understanding of the issues outlined, the Sustainability Dynamics Framework is applied to conduct a sustainability dynamic assessment for the USA and China 1995-2018. The results indicate both countries are consistently unsustainable due to significant levels of triumvirate-based factors, with differing dynamic trends, characteristics, and potential outcomes. The current static nature of the USA's environment-human system provides the opportunity for a managed co-evolutionary strategy. China's dynamic trends indicate a significant increasingly stressed environment-human system, which is at risk of a potential systemic crisis or collapse unless stringent corrective measures are implemented. In order for both countries to return to sustainability, there needs to be population mitigation to return them to optimum levels, and the use of contractionary policies and rapid decarbonation of their economies and societies. Unless this occurs, then their continued reticence to undertaking the urgent and necessary measures required not only risking their own future, but the world as well.

Eigenfrequency-based topology optimization using cooperative coevolutionary strategies and moving morphable components

Eigenfrequency-based topology optimization using cooperative coevolutionary strategies and moving morphable components

UCAV Path Planning for Avoiding Obstacles using Cooperative Co-evolution Spider Monkey Optimization

The unmanned combat aerial vehicle (UCAV) path planning problem is a complex global optimization problem that aims to seek an optimal flight route that avoids the threats and constraints on the battlefield. However, as the number and degree of the path threats increases, the recent UCAV path planning algorithms all suffer from being trapped into local optima with a low convergence rate, which leads to the poor performance. Therefore, to resolve these problems, we propose a Cooperative co-Evolution (CE)-based Spider Monkey Optimization (SMO) algorithm, named CESMO to address UCAV path planning problem for avoiding obstacles. First, a cooperative co-evolution strategy is proposed to prevent the algorithms from falling into local optima easily, enhancing the coherence of the path. After that, a further division is applied to the algorithm for diversifying the search direction of each individual in the sub-groups. Finally, the spider monkey optimization-based search mechanism is designed to further strengthen the search efficiency and boost the convergence rate. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and stability of the final paths.

Carnivore coexistence facilitated by spatial and dietary partitioning and fine‐scale behavioural avoidance in a semi‐arid ecosystem

AbstractSympatric carnivores compete for similar resources which may lead to dominant species influencing the ecology of subordinate ones. However, carnivores often make use of coevolutionary strategies which enable them to minimise competition with dominant competitors and thus facilitate coexistence. We used camera trapping and scat analysis to investigate the potential competition between leopards (Panthera pardus) and caracals (Caracal caracal) along spatial, temporal and dietary axes to determine the mechanisms of their coexistence in the Cederberg, South Africa. Our results showed that both carnivores co‐occurred at 39.73% of camera trapping sites, but spatial overlap based on Pianka's index was relatively low. We found a high overlap in daily activity patterns between these carnivore species both during winter and summer. Leopards and caracals exhibited fine‐scale behavioural avoidance of one another, with time‐to‐encounters between interspecific competitors being significantly larger compared to intraspecific competitors. The two carnivores had a relatively high diet overlap, although this was largely due to one prey species, namely rock hyrax (Procavia capensis), which appears to be an important shared prey item. However, we did find evidence of dietary niche segregation, as leopards consumed larger prey compared to caracals. Our study suggests that carnivore coexistence in the Cederberg is facilitated by a combination of population‐level partitioning in space‐use and dietary habits, as well as fine‐scale behavioural avoidance at the individual level to reduce interference competition. This study provides insights into the competition and coexistence mechanisms between sympatric carnivores and broadens our understanding of these ecological processes in carnivore guild systems.

A wholistic optimization of containerized workflow scheduling and deployment in the cloud–edge environment

Digital Twin in Industry 4.0 utilizes Internet of Things (IoT) to collect real-life data and combine it with simulation models for product design and development. The simulation process can be executed as a workflow, consisting of tasks with precedence constraints. In a container-based workflow execution system, each task in the workflow is executed in a container within a virtual machine (VM). In this paper, a three-step scheduling model is proposed to combine scheduling of container-based workflows and the deployment of containers on a cloud–edge environment. In the first step, virtual CPU (vCPU) is allocated for each container to enable vCPU sharing among different containers. Next, two-step resource deployment is used to schedule the containers onto VM, and VM onto the physical machines in either edge or cloud environment. Multiple objectives are considered, including minimizing makespan, load imbalance, and energy consumption, from the perspective of cloud–edge resources as well as containerized workflows. To obtain a set of non-dominated solutions, three evolution strategies are designed and combined with two multi-objective algorithm frameworks — co-evolution strategy (CES), basic non-co-evolution strategy (B-NCS), and hybrid non-co-evolution strategy (H-NCS). Simulation results demonstrate that the proposed model outperforms the existing two-step scheduling model and H-NCS performs better than other strategies.

A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems.

In this paper, a multipopulation dynamic adaptive coevolutionary strategy is proposed for large-scale optimization problems, which can dynamically and adaptively adjust the connection between population particles according to the optimization problem characteristics. Based on analysis of the network evolution characteristics of collaborative search between particles, a dynamic adaptive evolutionary network (DAEN) model with multiple interconnection couplings is established in this algorithm. In the model, the swarm type is divided according to the judgment threshold of particle types, and the dynamic evolution of collaborative topology in the evolutionary process is adaptively completed according to the coupling connection strength between different particle types, which enhances the algorithm’s global and local searching capability and optimization accuracy. Based on that, the evolution rules of the particle swarm dynamic cooperative search network were established, the search algorithm was designed, and the adaptive coevolution between particles in different optimization environments was achieved. Simulation results revealed that the proposed algorithm exhibited a high optimization accuracy and converging rate for high-dimensional and large-scale complex optimization problems.

Engineering a Supersecreting Strain of Escherichia coli by Directed Coevolution of the Multiprotein Tat Translocation Machinery.

Escherichia coli remains one of the preferred hosts for biotechnological protein production due to its robust growth in culture and ease of genetic manipulation. It is often desirable to export recombinant proteins into the periplasmic space for reasons related to proper disulfide bond formation, prevention of aggregation and proteolytic degradation, and ease of purification. One such system for expressing heterologous secreted proteins is the twin-arginine translocation (Tat) pathway, which has the unique advantage of delivering correctly folded proteins into the periplasm. However, transit times for proteins through the Tat translocase, comprised of the TatABC proteins, are much longer than for passage through the SecYEG pore, the translocase associated with the more widely utilized Sec pathway. To date, a high protein flux through the Tat pathway has yet to be demonstrated. To address this shortcoming, we employed a directed coevolution strategy to isolate mutant Tat translocases for their ability to deliver higher quantities of heterologous proteins into the periplasm. Three supersecreting translocases were selected that each exported a panel of recombinant proteins at levels that were significantly greater than those observed for wild-type TatABC or SecYEG translocases. Interestingly, all three of the evolved Tat translocases exhibited quality control suppression, suggesting that increased translocation flux was gained by relaxation of substrate proofreading. Overall, our discovery of more efficient translocase variants paves the way for the use of the Tat system as a powerful complement to the Sec pathway for secreted production of both commodity and high value-added proteins.

Co-evolution-based parameter learning for remote sensing scene classification

The appropriate setting of hyperparameter is a key factor to determine the performance of the deep learning model. Efficient hyperparametric optimization algorithm can not only improve the efficiency and speed of model hyperparametric optimization, but also reduce the application threshold of deep learning model. Therefore, we propose a parameter learning algorithm-based co-evolutionary for remote sensing scene classification. First, a co-evolution framework is proposed to optimize the optimizer’s hyperparameters and weight parameters of the convolutional neural networks (CNNs) simultaneously. Second, with the strategy of co-evolution with two populations, the hyperparameters can learn within the population and the weights of CNN can be updated by utilizing information between the populations. Finally, the parallel computing mechanism is adapted to speed up the learning process, as the two populations can evolve simultaneously. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed approach.

Co-Evolutionary Paradigm of Ecological Education for Sustainable Development: Methodological Basis

Throughout the history of its development, environmental education has been viewed as a priority area in the modernization of all educational systems in the face of aggravated environmental problems. A comparative analysis of the change in environmental education paradigms made it possible to conclude that they evolved in accordance with the change in cognitive models and value-target guidelines: environmental education, environmental education and environmental education for sustainable development. On the basis of a generalization of philosophical, scientific, psychological and pedagogical research, the statement is substantiated that in the modern conditions of the global environmental crisis, the relevance and importance of developing the methodological foundations of environmental education based on a co-evolutionary strategy, which ensures the transition of mankind to the path of sustainable development, is obvious. The article reveals the methodological foundations of the co-evolutionary strategy of environmental education for sustainable development, which reflects the cognitive model, value co-evolutionary relations and the constructive coherent-creative orientation of nature-friendly activity. The co-evolutionary paradigm of environmental education for sustainable development, based on the ideas of synergy, reflects the picture of the world of a post-industrial society. The essence of co-evolutionary subject-activity, transdisciplinary, integrative and integrative-situational, cultural-ecological coherent-creative approaches is revealed. The conclusion is made about the semantic and value-worldview conjugation of these approaches, which ensures the integrity of the methodological foundations of the co-evolutionary paradigm of environmental education for sustainable development. The experience of the Nizhny Novgorod scientific school of environmental education in the implementation of the considered methodological approaches in environmental education for sustainable development is presented. The promising directions of research on this problem are formulated, related to the determination of the functional completeness of the methodological foundations and the development of methods for their implementation in educational practice. Keywords: co-evolutionary paradigm, environmental education for sustainable development, methodology

Феномен космизма в контексте формирования антропоприродной парадигмы

The article considers the problem of transition to the anthroponatural paradigm of development, which is relevant for the modern stage of the world civilization process. It is noted that the global environmental crisis, the signs of which were clearly identifi ed at the turn of the 20th – 21st centuries, was the result of unbalanced transformative activities of people, as a result of which the very possibility of life on the planet was threatened. The current situation has shown the need for fundamental changes both in the existing type of management, based on unregulated consumption of natural resources, and in public consciousness in the direction of reorienting it to a rational attitude towards the environment, which would ensure the survival and further progressive movement of civilization. In the development of a coevolutionary strategy for the interaction of society and nature, based on a new system of moral values, the natural scientific, religious, philosophical and literary and artistic heritage of cosmism has significant spiritual potential. Based on the analysis of the works of representatives of Russian cosmism, it is concluded that the idea of the active role of humanity in transforming the environment involves, in addition to improving the scientifi c and technical sphere, the development of worldview attitudes focused on understanding a man as an organic part of natural (cosmic) unity, developing according to general evolutionary laws. The current sociocultural situation, characterized by a number of global problems (environmental, raw materials, food, etc.), indicates the need to move to an anthroponatural paradigm of civilizational development, which could ensure the joint evolution of people and nature, the formation of appropriate ethical standards and principles of ecological thinking. Clearly, the future of mankind, the prospects of which were outlined by the representatives of cosmism, is impossible without reliance on a solid moral foundation, thanks to which the population of the Earth has the opportunity not only to overcome the crisis consequences of its own activities on the planet, to carry out rational and balanced environmental management, but also to enter the next round of its development – the active exploration of outer space.

A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is the seminal framework of multiobjective evolutionary algorithms (MOEAs). To alleviate the nonuniformly distributed solutions generated by a fixed set of evenly distributed weight vectors in the presence of nonconvex and disconnected problems, an adaptive vector generation mechanism is proposed. A coevolution strategy and a vector generator are synergistically cooperated to remedy the weight vectors. Optimal weight vectors are generated to replace the useless weight vectors to assure that optimal solutions are distributed evenly. Experiment results indicate that this mechanism is efficient in improving the diversity of MOEA/D.

Study on Promoting Intelligent Manufacturing Path Choice of Manufacturing Enterprises Based on Coevolution Strategy

The basic guarantee of promoting intelligent manufacturing ability of manufacturing enterprises is to enhance the comprehensive strength of the country and realize industrialization. Based on the coevolution strategy, considering the time and space structure, this paper determines that the path of manufacturing enterprises to promote intelligent manufacturing is adaptive, visionary, planned, and emerging. According to the factors influencing the promotion of intelligent manufacturing by manufacturing enterprises, the paper proposes the following steps: adaptive strategy path, vision strategic path, planned strategic path, and emerging strategy path. The paper constructs an evaluation system for manufacturing enterprises to promote the selection of intelligent manufacturing path with 25 evaluation indexes and establishes the three-dimensional model of promoting intelligent manufacturing path selection by using the combination of Choquet fuzzy integral method and SPACE method. Through the empirical analysis of ARR, it is found that the planned strategic path is the most suitable path to promote intelligent manufacturing, and the scientificity and objectivity of the three-dimensional path selection model are shown. It is of great theoretical innovation and practical significance to develop a new perspective for manufacturing enterprises to promote the research and selection of intelligent manufacturing path.

Big data analytics in Industry 4.0 ecosystems

Big data analytics in Industry 4.0 ecosystems

Evolutionarily Stable Coevolution Between a Plastic Lytic Virus and Its Microbial Host.

Hosts influence and are influenced by viral replication. Cell size, for example, is a fundamental trait for microbial hosts that can not only alter the probability of viral adsorption, but also constrain the host physiological processes that the virus relies on to replicate. This intrinsic connection can affect the fitness of both host and virus, and therefore their mutual evolution. Here, we study the coevolution of bacterial hosts and their viruses by considering the dependence of viral performance on the host physiological state (viral plasticity). To this end, we modified a standard host-lytic phage model to include viral plasticity, and compared the coevolutionary strategies emerging under different scenarios, including cases in which only the virus or the host evolve. For all cases, we also obtained the evolutionary prediction of the traditional version of the model, which assumes a non-plastic virus. Our results reveal that the presence of the virus leads to an increase in host size and growth rate in the long term, which benefits both interacting populations. Our results also show that viral plasticity and evolution influence the classic host quality-quantity trade-off. Poor nutrient environments lead to abundant low-quality hosts, which tends to increase viral infection time. Conversely, richer nutrient environments lead to fewer but high-quality hosts, which decrease viral infection time. Our results can contribute to advancing our understanding of the microbial response to changing environments. For instance, both cell size and viral-induced mortality are essential factors that determine the structure and dynamics of the marine microbial community, and therefore our study can improve predictions of how marine ecosystems respond to environmental change. Our study can also help devise more reliable strategies to use phage to, for example, fight bacterial infections.

An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application

In order to overcome the slow convergence speed, poor global search ability and difficult designing rotation angle of quantum-inspired evolutionary algorithm (QEA), an improved quantum-inspired cooperative co-evolution algorithm based on combining the strategies of cooperative co-evolution, random rotation direction and Hamming adaptive rotation angle, namely MSQCCEA is proposed, which is employed to propose a new airport gate allocation optimization method in this paper. In the proposed MSQCCEA, the cooperative co-evolution strategy is used to improve the global search capability. The random rotation direction strategy is developed to change the quantum evolution direction from one to two in order to avoid local optimal solution and realize the full search of the solution space. A new Hamming adaptive rotation angle strategy is designed to enable individuals to adaptively adjust the rotation angle according to the difference degree between the individual and the target individual, so as to improve the global search ability and convergence speed. A new airport gate allocation optimization method using MSQCCEA is realized to effectively allocate airport gates to the flights. Finally, the knapsack problem and the actual airport gate allocation problem are used to verify the effectiveness of the proposed MSQCCEA and gate allocation optimization method, respectively. The comparison experiment results demonstrate that the proposed MSQCCEA has faster convergence speed and higher convergence accuracy, and the proposed gate allocation optimization method takes on great potential to make decisions for actual airport management.