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The gravitational search algorithm is a global optimization algorithm that has the advantages of a swarm intelligence algorithm. Compared with traditional algorithms, the performance in terms of global search and convergence is relatively good, but the solution is not always accurate, and the algorithm has difficulty jumping out of locally optimal solutions. In view of these shortcomings, an improved gravitational search algorithm based on an adaptive strategy is proposed. The algorithm uses the adaptive strategy to improve the updating methods for the distance between particles, gravitational constant, and position in the gravitational search model. This strengthens the information interaction between particles in the group and improves the exploration and exploitation capacity of the algorithm. In this paper, 13 classical single-peak and multi-peak test functions were selected for simulation performance tests, and the CEC2017 benchmark function was used for a comparison test. The test results show that the improved gravitational search algorithm can address the tendency of the original algorithm to fall into local extrema and significantly improve both the solution accuracy and the ability to find the globally optimal solution. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the basic Gravity search algorithm（GSA）, such as prone to premature maturity, easy to fall into local optimum, and lack an effective acceleration mechanism, this paper proposed a GSA（IABHGSA） based on the improved adaptive black hole mechanism. The algorithm used the improved Tent mapping to initialize the population, which makes the distribution of the initial population more random, uniform and traversal, and it enhanced the global exploration capability of the algorithm; The algorithm introduced an improved adaptive black hole mechanism and selected the position update strategy, according to the evolution of the particles, which makes the position update more reasonable, Effectively reduce the possibility of particles falling into local optima; Through the optimal and worst particle update strategy based on learning ideas, enhanced the algorithm’s ability to escape from local optima and improved the algorithm’s optimization speed; The algorithm introduced group migration to provide effective accelerated convergence mechanism for the algorithm. Finally, this paper selected 8 benchmark functions to test IABHGSA and compared with the experimental results of related algorithms, the results show that IABHGSA has better optimization performance. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the original monarch butterfly optimization algorithm is easy to fall into the local optimal solution and has poor convergence, an adaptive monarch butterfly optimization algorithm based on mutation reverse learning is proposed. Firstly, the mutation idea of genetic algorithm is combined with the reverse learning strategy to replace the original transfer operator to improve the global convergence. Then, an adaptive strategy is integrated into the adjustment operator of the original monarch butterfly optimization algorithm to make the population more diverse. Finally, Cauchy mutation will be carried out among the last five monarch butterflies in the updated population, which will generate greater disturbance near the mutated individuals and make the whole population search for optimization in a larger range. In order to verify the improved monarch butterfly optimization algorithm, benchmark function and Wilcoxon rank sum test are used to test it. The experimental results show that the convergence speed and optimization accuracy of the improved algorithm are greatly improved. [ABSTRACT FROM AUTHOR]

Inverse kinematic solution is a critical procedure for the control of manipulators; however, only a small number of manipulators that meet certain conditions have closed-form solutions. In this paper, the conventional solution was replaced by the optimization process of a proposed fitness function. The fitness function aims to obtain a set of accurate joint angles for the desired position and orientation of the end effector. The function is based on forward kinematics; hence, this approach can avoid singularities and be suitable for solving many manipulators. For solving this optimization problem, particle swarm optimization algorithm was chosen as the basis due to its easy principle and excellent performance. To enhance the performance of the algorithm in searching global optimum, an adaptive inertia weight strategy applied to adjust the velocity of particles was proposed. Besides, for particles, to avoid trapping in local optimum at the boundary, a special boundary treatment was presented. In this study, two serial robotic manipulators were used to test the performance of adaptive particle swarm optimization (APSO). Meanwhile, several powerful PSO variants improved from disparate methods were selected to compare with APSO. The experimental results demonstrate that the proposed fitness function with APSO can solve the inverse kinematic problem of multi-DOF manipulators efficiently and accurately. [ABSTRACT FROM AUTHOR]

Multi-objective particle swarm optimization algorithms encounter significant challenges when tackling many-objective optimization problems. This is mainly because of the imbalance between convergence and diversity that occurs when increasing the selection pressure. In this paper, a novel adaptive MOPSO (ANMPSO) algorithm based on R2 contribution and adaptive method is developed to improve the performance of MOPSO. First, a new global best solutions selection mechanism with R2 contribution is introduced to select leaders with better diversity and convergence. Second, to obtain a uniform distribution of particles, an adaptive method is used to guide the flight of particles. Third, a re-initialization strategy is proposed to prevent particles from trapping into local optima. Empirical studies on a large number (64 in total) of problem instances have demonstrated that ANMPSO performs well in terms of inverted generational distance and hyper-volume metrics. Experimental studies on the practical application have also revealed that ANMPSO could effectively solve problems in the real world. [ABSTRACT FROM AUTHOR]

Optimization techniques combined with uncertainty quantification are computationally expensive for robust aerodynamic optimization due to expensive CFD costs. Surrogate model technology can be used to improve the efficiency of robust optimization. In this paper, non-intrusive polynomial chaos method and Kriging model are used to construct a surrogate model that associate stochastic aerodynamic statistics with airfoil shapes. Then, global search algorithm is used to optimize the model to obtain optimal airfoil fast. However, optimization results always depend on the approximation accuracy of the surrogate model. Actually, it is difficult to achieve a high accuracy of the model in the whole design space. Therefore, we introduce the idea of adaptive strategy to robust aerodynamic optimization and propose an adaptive stochastic optimization framework. The surrogate model is updated adaptively by increasing training airfoils according to historical optimization results to guarantee the accuracy near the optimal design point, which can greatly reduce the number of training airfoils. The proposed method is applied to a robust aerodynamic shape optimization for drag minimization considering uncertainty of Mach number in transonic region. It can be concluded that the proposed method can obtain better optimal results more efficiently than the traditional robust optimization method and global surrogate model method. [ABSTRACT FROM AUTHOR]

Trust region method is a robust method for optimization problems. In this paper, we propose a novel adaptive nonmonotone technique based on trust region methods for solving unconstrained optimization. In order to accelerate the convergence of trust region methods, an adaptive trust region is generated according to the Hessian of the iterate point. Both the nonmonotone techniques and this adaptive strategies can improve the trust region methods in the sense of convergence. We prove that the proposed method is locally superlinear convergence under some standard assumptions. Numerical results show that the new method is effective and has a high speed of convergence in practice. [ABSTRACT FROM AUTHOR]

This study concerns with a trust-region-based method for solving unconstrained optimization problems. The approach takes the advantages of the compact limited memory BFGS updating formula together with an appropriate adaptive radius strategy. In our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-Newton formulas helps to handle large-scale problems. Theoretical analysis indicates that the new approach preserves the global convergence to a fi rst-order stationary point under classical assumptions. Moreover, the superlinear and the quadratic convergence rates are also established under suitable conditions. Preliminary numerical experiments show the effectiveness of the proposed approach for solving large-scale unconstrained optimization problems. [ABSTRACT FROM AUTHOR]

In the seismic inversion model, as the neural network algorithm there are some problems, the convergence bad, accuracy is not high. This paper presents a seismic inversion based on artificial fish Swarm Optimization neural network models. First to initialize fish mapping of chaos optimization and ergodicity of artificial fish-swarm search and adaptive strategies of artificial fish-swarm algorithm of optimization search strategy, and using as a parameter change measure, in the course of operation of the algorithm Adaptive adjustment of parameters, and finally building seismic inversion based on artificial fish Swarm Optimization neural network model. Simulation experiments show that improved artificial fish-swarm algorithm presented in this paper has better accuracy optimization, artificial fish-swarm algorithm of neural network based on improved convergence properties better and seismic inversion algorithm based on improved neural network model of meanvariance changes more slowly. [ABSTRACT FROM AUTHOR]

This paper proposes an improved ant colony system with adaptive strategies, called α -AACS and considers its performance. First of all, we introduce α-nearness based on the minimum 1-tree for the disadvantage of the Ant Colony System (ACS), which better reflects the chances of a given link, being a member of an optimal tour. Next, we utilize the adaptive operator to balance the population diversity and the convergence speed and propose other optimizations for ACS. Finally, we present an account of the experiments and the statistic-based analysis, which clearly shows that α-AACS has a better global searching ability in finding the best solutions and better performance in solution variation. [ABSTRACT FROM AUTHOR]

This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimization problems. The GDE algorithm provides a generalized evolution process based on two mutation operations to enhance search capability. Initially, all individuals in the population are grouped into a superior group and an inferior group based on their fitness values. The two groups perform different mutation operations. The local mutation model is applied to individuals with better fitness values, i.e., in the superior group, to search for better solutions near the current best position. The global mutation model is applied to the inferior group, which is composed of individuals with lower fitness values, to search for potential solutions. Subsequently, the GDE algorithm employs crossover and selection operations to produce offspring for the next generation. In this paper, an adaptive tuning strategy based on the well-known 1/5th rule is used to dynamically reassign the group size. It is thus helpful to trade off between the exploration ability and the exploitation ability. To validate the performance of the GDE algorithm, 13 numerical benchmark functions are tested. The simulation results indicate that the approach is effective and efficient. [ABSTRACT FROM AUTHOR]

Abstract: A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse analysis of computationally intensive physics-based models. Typically, models are calibrated and model parameters are estimated by minimization of the discrepancy between model simulations characterizing the system and existing observations requiring a substantial number of model evaluations. Squads is designed to be computationally efficient and robust in identification of the global optimum (i.e. maximum or minimum value of an objective function). It integrates global and local optimization using Adaptive Particle Swarm Optimization (APSO) and Levenberg–Marquardt (LM) optimization using adaptive rules based on runtime performance. The global strategy (APSO) optimizes the location of a set of solutions (particles) in the parameter space. The local strategy (LM) is applied only to a subset of the particles at different stages of the optimization based on the adaptive rules. After the LM adjustment of the subset of particle positions, the updated particles are returned to APSO. Therefore, squads is a global strategy that utilizes a local optimization speedup. The advantages of coupling APSO and LM in the manner implemented in squads is demonstrated by comparisons of squads performance against Levenberg–Marquardt (LM), Particle Swarm Optimization (PSO), Adaptive Particle Swarm Optimization (APSO; i.e. TRIBES), and an existing hybrid optimization strategy (hPSO). All the strategies are tested on 2D, 5D and 10D Rosenbrock and Griewank polynomial test functions and a synthetic hydrogeologic application to identify the source of a contaminant plume in an aquifer. Tests are performed using a series of runs with random initial guesses for the estimated parameters. The performance of the strategies are compared based on their robustness, defined as the percentage of runs that identify the global optimum, and their efficiency, quantified by a statistical representation of the number of function evaluations performed prior to identification of the global optimum. Squads is observed to have better performance than the other strategies for the test functions and the hydrogeologic application when both robustness and efficiency are taken into consideration. [Copyright &y& Elsevier]

The goal of sustainable water quality management is to keep total pollutant discharges from exceeding the assimilation capacity of a water body. Climate change may influence streamflows, and further alter assimilation capacity and degrade river sustainability. The purposes of this study are to evaluate the effect of climate change on sustainable water quality management and design an early warning indicator to issue warnings on river sustainability. A systematic assessment procedure is proposed here, including a weather generation model, the streamflow component of GWLF, QUAL2E, and an optimization model. The Touchen creek in Taiwan is selected as the study area. Future climate scenarios derived from projections of four global climate models (GCMs) and two pollutant discharge scenarios, as usual and proportional to population, are considered in this study. The results indicate that streamflows may very likely increase in humid seasons and decrease in arid seasons, respectively. The reduction of streamflow in arid seasons may further degrade water quality and assimilation capacity. In order to provide warnings to trigger necessary adaptation strategies, an early warning indicator is designed and its 30-year moving average is calculated. Finally, environmental monitoring systems and methods to prioritize adaptation strategies are discussed for further studies in the future. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, optimal control theory is used to minimize the total mean drag for a circular cylinder wake flow in the laminar regime (). The control parameters are the amplitude and the frequency of the time-harmonic cylinder rotation. In order to reduce the size of the discretized optimality system, a proper orthogonal decomposition (POD) reduced-order model (ROM) is derived to be used as state equation. We then propose to employ the trust-region proper orthogonal decomposition (TRPOD) approach, originally introduced by Fahl [M. Fahl, Trust-region methods for flow control based on reduced order modeling, Ph.D. Thesis, Trier University, 2000], to update the reduced-order models during the optimization process. A lot of computational work is saved because the optimization process is now based only on low-fidelity models. A particular care was taken to derive a POD ROM for the pressure and velocity fields with an appropriate balance between model accuracy and robustness. The key enablers are the extension of the POD basis functions to the pressure data, the use of calibration methods for the POD ROM and the addition in the POD expansion of several non-equilibrium modes to describe various operating conditions. When the TRPOD algorithm is applied to the wake flow configuration, this approach converges to the minimum predicted by an open-loop control approach and leads to a relative mean drag reduction of 30% at reduced cost. [Copyright &y& Elsevier]

• A chaos-based non-linear inertia weight is used to balance capacities better. • Stochastic and mainstream learning strategies are devised to enhance diversity. • An adaptive position updating strategy further adjusts balance. • A terminal replacement mechanism is adopted to enhance convergence precise. In expert systems, complex optimization problems are usually nonlinear, nonconvex, multimodal and discontinuous. As an efficient and simple optimization algorithm, particle swarm optimization(PSO) has been widely applied to solve various real optimization problems in expert systems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains an open issue. To overcome these drawbacks and strengthen the ability of PSO in solving complex optimization problems, a modified PSO using adaptive strategy called MPSO is proposed. In MPSO, in order to well balance the global exploration and local exploitation capabilities of the PSO, a chaos-based non-linear inertia weight is proposed. Meanwhile, to avoid the premature convergence, stochastic and mainstream learning strategies are adopted. Finally, an adaptive position updating strategy and terminal replacement mechanism are employed to enhance PSO's ability to solve complex optimization problems in expert systems. 30 complex CEC2017 benchmark functions are utilized to verify the promising performance of MPSO, experimental results and statistical analysis indicate that MPSO has competitive performance compared with 16 state-of-the-art algorithms. The source code of MPSO is provided at https://github.com/lhustl/MPSO. [ABSTRACT FROM AUTHOR]

We propose a high-order adaptive spectral element method based on Gegenbauer polynomial basis for solving singularly perturbed differential equations with Dirichlet boundary conditions and a boundary layer at one end. The method reduces the singularly perturbed boundary value problem into a single system of linear algebraic equations when the solution is smooth on the solution domain, or two systems of linear algebraic equations when the solution has a thin boundary layer. The core of the new adaptive strategy lies in constructing a mathematical model of the last few Gegenbauer coefficients in the Gegenbauer truncated series approximating the second-order derivative of the solution, which can be readily converted into a minimization problem in a least-squares sense and solved using fast optimization techniques. An error and convergence analysis of the method for sufficiently smooth solutions is presented. A numerical test example with various parameter settings is presented to verify the accuracy, effectiveness, and applicability of the proposed method. Numerical comparisons with other rival methods in the literature are included to demonstrate further the power of the proposed method. [ABSTRACT FROM AUTHOR]