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Following two decades of sustained studies, metaheuristic algorithms have made considerable achievements in the field of multi-objective optimization problems (MOPs). However, under most existing metaheuristic frameworks, an improved scheme introduced to address specific defects usually leads to additional problems that need to be solved further. Emerging optimization mechanisms should be considered to break the bottleneck, and an adaptive multi-objective competitive swarm optimization (AMOCSO) algorithm, a promising option for solving MOPs, is proposed in this paper. Firstly, the competitive mechanism is modified so that it can perform well on MOPs, and an improved learning scheme is designed for the winners and the losers, which can greatly enhance the optimization efficiency and balance the convergence and the diversity of the proposed algorithm. Then, an external archive and its maintenance schemes are introduced to prevent the population from degenerating and make the algorithm framework more comprehensive. Moreover, a practical adaptive strategy is proposed to fill the blank of parameter research, and no human factors exist in AMOCSO, which means that an amazing promotion can be achieved in generalization. Finally, abundant experimental studies are carried out, and the results of comparative experiments show that the proposed algorithm has significant advantages over several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has been employed to solve numerous real-world problems because of its strong optimization ability and easy implementation. However, PSO still has some shortcomings in solving complicated optimization problems, such as premature convergence and poor balance between global exploration and local exploitation. A novel hybrid particle swarm optimization using adaptive strategy (ASPSO) is developed to address associated difficulties. The contribution of ASPSO is threefold: (1) a chaotic map and an adaptive position updating strategy to balance exploration behavior and exploitation nature in the search progress; (2) elite and dimensional learning strategies to enhance the diversity of the population effectively; (3) a competitive substitution mechanism to improve the accuracy of solutions. Based on various functions from CEC 2017, the numerical experiment results demonstrate that ASPSO is significantly better than the other 16 optimization algorithms. Furthermore, we apply ASPSO to a typical industrial problem, the optimization of melt spinning progress, where the results indicate that ASPSO performs better than other algorithms. [ABSTRACT FROM AUTHOR]

Multitask optimisation, which aims to simultaneously handle multiple optimisation problems, has attracted considerable attention for solving many complex optimisation problems. However, as the number of tasks increases, selecting similar tasks and transferring useful knowledge become increasingly difficult tasks, further leading to negative transfers and affecting the algorithm performance. In this study, we propose a knowledge-reuse-based multitask evolutionary algorithm (KR-MTEA), that includes two strategies: knowledge-reuse-based knowledge transfer and adaptive strategies. The knowledge-reuse-based knowledge transfer strategy transforms knowledge to be transferred while considering the convergence interval of the decision variables and makes it more suitable for the current task. For the adaptive strategy, the concept of advantage is proposed to adapt to similar tasks and transfer probabilities. In addition, the proposed knowledge transfer strategy is applied to the self-evolution within the task to improve the performance of the KR-MTEA. On four benchmark test suites with 2, 10, 50, and 50 tasks, the KR-MTEA demonstrated its competitiveness in solving multitask and many-task optimisation problems compared with other advanced multitask and many-task algorithms. [ABSTRACT FROM AUTHOR]

• Temporal correlation among snapshots is important for RB targeting flow nonlinearity; • Global RB approaches can be inaccurate while recovering nonlinear flow dynamics; • The Local Adaptive RB Framework provides better accuracy with respect to Global RB; • The Adaptive RB has been demonstrated on Impulsively started vortex-dominated flows. A local adaptive Reduced Basis approach is presented with a focus on the accuracy in reconstructing unsteady vortex-dominated flow fields generated by impulsively started lifting surfaces and bodies. The adaptive approach is built on the combination of methods based on Proper Orthogonal Decomposition, Spectral Orthogonal Decomposition, Dynamic Mode Decomposition and Recursive Dynamic Mode Decomposition and aims at making possible to explicitly take into account the temporal correlation between available snapshots of the problem. The latter has proven to be a critical element when building a Reduced Model for complex non-periodic and nonlinear unsteady flows. For a given instant of time in which the reconstruction is needed, the most accurate Reduced Model is automatically selected on the basis of an estimation of the error of the various methods in reconstructing the entire flow field. The impulsive start of a 2D symmetric airfoil, which evolves towards a periodic limit cycle, and the impulsive start of a three element airfoil and a 3D wing in high-lift configurations will be considered as demonstration cases. [ABSTRACT FROM AUTHOR]

Highlights • An adaptive version of the Particle Swarm Optimization algorithm is proposed. • All the parameters of the algorithm are calculated during the optimization process. • Comparison with other PSO implementations and other algorithms from the literature. • The algorithm is ranked in the 3 most effective algorithms applied for the VRPTW. • The algorithm is tested in 356 instances with nodes varying between 100 and 1000. Abstract In this paper, a new variant of the Particle Swarm Optimization (PSO) algorithm is proposed for the solution of the Vehicle Routing Problem with Time Windows (VRPTW). Three different adaptive strategies are used in the proposed Multi-Adaptive Particle Swarm Optimization (MAPSO) algorithm. The first adaptive strategy concerns the use of a Greedy Randomized Adaptive Search Procedure (GRASP) that is applied when the initial solutions are produced and when a new solution is created during the iterations of the algorithm. The second adaptive strategy concerns the adaptiveness in the movement of the particles from one solution to another where a new adaptive strategy, the Adaptive Combinatorial Neighborhood Topology, is used. Finally, there is an adaptiveness in all parameters of the Particle Swarm Optimization algorithm. The algorithm starts with random values of the parameters and based on some conditions all parameters are adapted during the iterations. The algorithm was tested in the two classic sets of benchmark instances, the one that includes 56 instances with 100 nodes and the other that includes 300 instances with number of nodes varying between 200 and 1000. The algorithm was compared with other versions of PSO and with the best performing algorithms from the literature. [ABSTRACT FROM AUTHOR]

Abstract The particle filter is a class of sequential Monte Carlo method, which can be applied to any nonlinear and non-Gaussian random system that can be represented by the state space model. However, the resampling procedure in the particle filter may introduce particle impoverishment. To mitigate this problem, an adaptive genetic particle filter is developed in this paper. It employs an unscented Kalman filter for a low-dimensional state estimation and a cubature Kalman filter for a high-dimensional system to generate the importance proposal distribution to take full advantage of the latest measurements within a particle filter framework. The crossover and improved mutation strategies are used together to increase the particle diversity, where the loss of particle diversity is a specific manifestation of particle impoverishment. To optimize the selection of parameters, make the genetic algorithm converge to the global optimal solution and improve the work efficiency, an adaptive strategy is designed so that the mutation probability of the algorithm is self-determined. Two experiments, one is the univariate growth model, and one is the synthetic experiment, both show the superiority and effectiveness of the proposed algorithm. Finally, the adaptive genetic particle filter is applied to the attitude estimation system, and the results show that the novel algorithm possesses higher accuracy and faster convergence rate compared with the multiplicative extended Kalman filter and the unscented quaternion estimator. [ABSTRACT FROM AUTHOR]

An algorithm is sensitive to parameters; different parameter settings for solving optimization problems can thus have a serious impact on algorithm performance. This leads to an inability to determine the optimal set of parameters for the algorithm to solve the problem at hand. In this study, we propose an adaptive strategy with a multi-population multi-objective algorithm (A-MPMO) framework to select the appropriate set of genetic settings according to the problem to be solved and eliminate the sensitivity of the algorithm to the parameters. Multi-population are often combined with Evolutionary Algorithms (EAs) as an effective strategy to maintain population diversity. First, we divided the population generated by the algorithm into multiple subpopulations to expand the search range and updated them iteratively using operators with different genetic parameters. Second, based on multi-population, subpopulations compete for limited computational resources, implying that the size of each subpopulation adaptively adjusts according to the degree of its contribution to problem solving. Finally, a set of subpopulations that are best suited to solve the problem is selected to improve the adaptability to different problems. For DTLZ, ZDT, and UF, compared to the other algorithms, A-MPMO was experimentally shown to produce better performance. [ABSTRACT FROM AUTHOR]

Constrained optimization problems (COPs) are common in many fields. To solve such problems effectively, in this paper, we propose a new constrained optimization evolutionary algorithm (COEA) named CACDE that combines an adaptive trial vector generation strategy-based differential evolution (DE) algorithm with a cluster-replacement-based feasibility rule. In CACDE, some potential mutation strategies, scale factors and crossover rates are stored in candidate pools, and each element in the pools is assigned a selection probability. During the trial vector generation stage, the mutation strategy, scale factor and crossover rate for each target vector are competitively determined based on these selection probabilities. Meanwhile, the selection probabilities are dynamically updated based on statistical information learned from previous searches in generating improved solutions. Moreover, to alleviate the greediness of the feasibility rule, the main population is divided into several clusters, and one vector in each cluster is conditionally replaced with an archived infeasible vector with a low objective value. The superior performance of CACDE is validated via comparisons with some state-of-the-art COEAs over 2 sets of artificial problems and 5 widely used mechanical design problems. The results show that CACDE is an effective approach for solving COPs, basically due to the use of adaptive DE and cluster-replacement-based feasibility rule. [ABSTRACT FROM AUTHOR]

In this paper, the stabilization and synchronization of chaotic systems are investigated by means of intermittent control. At first, a generalized intermittent control and its adaptive strategy are introduced, in which the traditional periodic intermittent control and the aperiodic case are unified. Based on the designed control protocols, by applying comparison principle, the method of piecewise auxiliary function, piecewise analysis technique and the theory of series, some novel and effective criteria are derived to ensure the stabilization and synchronization of chaotic systems. Finally, two numerical examples are provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]

• An optimization method FiPLAM based on Hankel matrix nonconvex factorization is proposed to reconstruct NMR spectroscopy. • Nesterov momentum is introduced to accelerate convergence speed. • An adaptive strategy for the number of spectral peaks is utilized to make FiPLAM more flexible. • FiPLAM requires much smaller time and can obtain more reliable reconstruction than LRHMF and FIHT for realistic 2D NMR spectrum. Multidimensional nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful tools for qualitative or quantitative analysis of the composition and structure of various organic and inorganic substances. However, the time required to acquire NMR signals increases exponentially with dimensionality. Therefore, non-uniform sampling is commonly adopted to accelerate data acquisition and then the complete spectrum can be obtained by reconstruction method. At present, the state-of-the-art reconstruction methods are based on the idea of low-rank Hankel matrix completion and solved by different singular value thresholding methods. However, the computation of singular value decomposition (SVD) is very time-consuming, especially for high-resolution spectra. In this paper, we proposed a Hankel matrix nonconvex factorization optimization model to avoid SVD, thus greatly reducing the computational time. We developed a numerical algorithm based on the linearized Alternating direction method of multipliers to solve the proposed optimization problem, and Nesterov momentum is adopted to accelerate the convergence of the algorithm. However, for nonconvex model, the high quality reconstruction can only be obtained when the number of the spectral peaks is known. Thus we designed an adaptive strategy to alleviate this problem. Experiments are performed to demonstrate that the proposed algorithm can obtain higher quality reconstruction in less computational time than low rank Hankel matrix factorization and fast iterative hard thresholding methods. [ABSTRACT FROM AUTHOR]