Due to the complexity of many-objective optimization problems, the existing many-objective optimization algorithms cannot solve all the problems well, especially those with complex Pareto front. In order to solve the shortcomings of existing algorithms, this paper proposes a coevolutionary algorithm based on dynamic learning strategy. Evolution is realized mainly through the use of Pareto criterion and non-Pareto criterion, respectively, for two populations, and information exchange between two populations is used to better explore the whole objective space. The dynamic learning strategy acts on the non-Pareto evolutionary to improve the convergence and diversity. Besides, a dynamic convergence factor is proposed, which can be changed according to the evolutionary state of the two populations. Through these effective heuristic strategies, the proposed algorithm can maintain the convergence and diversity of the final solution set. The proposed algorithm is compared with five state-of-the-art algorithms and two weight-sum based algorithms on a many-objective test suite, and the results are measured by inverted generational distance and hypervolume performance indicators. The experimental results show that, compared with the other five state-of-the-art algorithms, the proposed algorithm achieved the optimal performance in 47 of the 90 cases evaluated by the two indicators. When the proposed algorithm is compared with the weight-sum based algorithms, 83 out of 90 examples achieve the optimal performance.

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

https://www.jseepub.com/CN/article/downloadArticleFile.do?attachType=PDF&id=6980

Improved method for subsystems performance trade-off in system-of-systems oriented design of UAV swarms

The systematic and network-centric warfare becomes complicated currently. It is a challenge problem to design and choose the optimal combat system-of-systems to complete missions. In order to solve this challenge, we propose a super-network model based combat system-of-systems architecture (CSoSA) and a scheme space exploration algorithm in this paper. First, a formal definition of CSoSA is given based on the operational capability generation elements. The framework for CSoSA scheme space exploration problem is then presented. The goal of the problem is to select the optimal architecture with maximized expected rewards and minimized cumulative costs. In addition, a novel and optimal algorithm for CSoSA dynamic exploration is proposed based on the greedy search algorithm. Finally, we evaluated our algorithm through simulation experiments, where the results showed that our algorithm is significantly better than several benchmark algorithms. In general, the proposed architecture framework and the exploration algorithm can assist commanders in making further decisions.

/pdf/an-optimal-approach-for-combat-system-of-systems-yl7tv7w4du.pdf

An Optimal Approach for Combat System-of-Systems Architecture Search Under Uncertainty

The research of Cooperative Operation System-of-Systems (COSoS) for manned aerial vehicles (MAV) and unmanned aerial vehicles (UAV) has garnered significant attention recently due to its potential to enhance the situational awareness, expand mission capabilities, and reduce pilot risk. A critical aspect of this research is the modeling and effectiveness evaluation of MAV/UAV COSoS. This article presents a review of current research on MAV/UAV COSoS modeling and evaluation methods. First, the definition of MAV/UAV COSoS is clarified, and its composition is described. Second, the latest research progress on MAV/UAV COSoS modeling methods based on standard framework-based modeling and complex network-based modeling is presented. Differences between the two types of methods in characterizing MAV/UAV COSoS are then summarized. Next, this article introduces the research progress on MAV/UAV COSoS effectiveness evaluation methods and concludes by summarizing the advantages and disadvantages of evaluation methods and evaluation index systems in MAV/UAV COSoS. Finally, future trends and possible research directions are suggested.

Research Progress in Modeling and Evaluation of Cooperative Operation System-of-systems for Manned-unmanned Aerial Vehicles

Preference-inspired coevolutionary algorithm with sparse autoencoder for many-objective optimization

With the rapid advancement of unmanned combat systems and the maturation of unmanned swarm technology, integrating unmanned combat systems into existing combat system of systems has become a hot topic However, the current research at the organizational level is basically nonexistent Therefore, we propose a methodology for designing an optimized architecture that is a significant product at this level to fill this technical gap The design process consists of two steps: architecture modeling and coevolutionary optimization The architecture model is composed of three basic elements, which are mission, equipment, and the command and control structure The selection of the best scheme from possible solutions is a multiobjective optimization problem, and its computational complexity increases rapidly with the increase in the scale of combat system of systems To overcome this difficulty, we introduce a decomposition-based evolutionary algorithm NSGA-III and improve it by using preference vectors to enhance the algorithm’s local search capability Finally, we conduct the comparative experiments on benchmark test suites and design an operation case to demonstrate the advance and effectiveness of the enhanced algorithm The proposed architecture modeling and optimization method can achieve a set of nondominated solutions and provide auxiliary decision-making information to help commanders make decisions and arrangements

Design of an Optimized Architecture for Manned and Unmanned Combat System-of-Systems: Formulation and Coevolutionary Optimization

It remains a challenge to identify a satisfactory set of tradeoff solutions for many-objective optimization problems that have more than three objectives. Coevolving the solutions with preference is becoming increasingly popular due to the enhanced local search capability, which makes it suitable for solving many-objective optimization problems. The framework of preference-inspired co-evolutionary algorithms (PICEAs) is suitable for obtaining promising performance for such problems, and the PICEA with goal vectors (PICEA-g) has achieved good performance in many applications. In this paper, an improved PICEA-g is proposed to further resolve this long-standing problem. The local principal component analysis operator is used as a controller to further expand the ability of the PICEA-g algorithm and enhance the convergence of PICEA-g. The proposed algorithm was evaluated using several widely used benchmark test suites that had 3–15 objectives and made a systematic comparison with five state-of-the-art multi-objective evolutionary algorithms. The resulting substantial amount of experimental results revealed that the algorithm we proposed could have good performance on most of the test suites assessed in our research, and it performs very well compared with other many-objective optimization algorithms. In addition, a sensitivity test was carried out to explore the impact of a key parameter in the algorithm we proposed in this study.

Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization

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Design of an Optimized Architecture for Manned and Unmanned Combat System-of-Systems: Formulation and Coevolutionary Optimization

Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization