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We propose the residual expansion (RE) algorithm: a global (or near-global) optimization method for nonconvex least squares problems. Unlike most existing nonconvex optimization techniques, the RE algorithm is not based on either stochastic or multi-point searches, therefore, it can achieve fast global optimization. Moreover, the RE algorithm is easy to implement and successful in high-dimensional...
Striking hostile ground targets for air force is a crucial way to achieve air superiority in modern warfare. For the maximum efficiency of limited munitions, the optimal weapon target assignment (WTA) scheme should be developed. Aimed at the fact that lots of approaches have been presented for the WTA problem of stationary targets, while less for the ones of moving targets, this paper proposes an...
To improve the measuring accuracy of planar curve profile error, an improved genetic algorithm is put forward to realize self-adaptive matching of measured curve, eliminating the position deviation during error evaluation of planar curve profile. It not only improves the efficiency and precision of the algorithm but also prevents premature convergence to local optimal solutions by introducing a relative...
Alternating direction method of multipliers (ADMM) is a promising approach to solve “big data” problems due to its efficient variable decomposition and fast convergence. However, it is subject to the following two fundamental assumptions: no contradiction among multiple controllers' objectives and ideal feedback from the agents to the controllers. In this paper, a multiple-leader multiple-follower...
This paper is concerned with a class of distributed nonsmooth convex constrained optimization problems with set constraints. The objective function is a sum of local convex functions, which are not necessarily differentiable. A new distributed continuous-time gradient-based algorithm using the decomposition design is explicitly constructed to solve the distributed optimization problem. Rigorous proofs...
A modified PRP conjugate gradient method is proposed in this paper based on the modified secant equation. The main properties of the new method are described as follows: (i) the parameter βk has not only gradient value information but also function value information; (ii) βk ≥ 0, ∀ k; (iii) the search direction generated by the presented method possesses both the sufficient descent and trust region...
Fruit fly optimization algorithm (FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified Fruit Fly Optimization Algorithm (SAMFOA) is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value...
Randomized gradient-free algorithms through sequential Gaussian smoothing are proposed for distributed optimization over time-varying random network, where the collective goal of agents is to minimize the sum of locally known cost functions. Each agent has access to its own nonsmooth convex function, constrained to a commonly known convex set. Based on sequential Gaussian smoothing of the objective...
Most of the existing community detection (CD) methods are designed primarily for unsigned networks containing only positive links. Therefore, it is significant to explore and design effective CD methods for signed social networks (SNs) with both positive and negative links. In this paper, we first utilize decomposable characteristic of modularity Q to establish a bi-objective model for community detection...
In this paper, a fully distributed strategy is proposed to solve the N-coalition multi-agent games. The agents in the considered N-coalition multi-agent games are supposed to have limited access to the other players' actions. Consensus protocols, including a leader-following consensus protocol and a dynamic average consensus protocol, are leveraged to search for the Nash equilibrium of the N-coalition...
We investigate the problem of distributed source seeking with velocity actuated and force actuated vehicles by developing distributed Kiefer-Wolfowitz algorithm. First, based on stochastic approximation algorithm with expanding truncations, we present the distributed Kiefer-Wolfowitz algorithm, in which two noisy observations of each agent's objective function is used to estimate its gradient and...
Image data is frequently extremely large and oftentimes pixel values are occluded or observed with noise. Additionally, images can be related to each other, as in images of a particular individual. This method augments the recently proposed Generalized Low Rank Model (GLRM) framework with graph regularization, which flexibly models relationships between images. For example, relationships might include...
Distributed algorithms are proposed to solve distributed optimization problems for a network of strongly connected agents in this paper. The proposed algorithms are based on a combination of a leader-following consensus protocol and the gradient descent method/primal-dual dynamics. In the leader-following consensus protocol, each agent acts as a virtual leader that provides its local measurements...
In this paper, we consider the energy efficiency maximization problem in downlink multi-input multi-output (MIMO) multi-cell systems, where all users suffer from inter-cell interference. To solve this optimization problem with a nonconcave objective function and a complex-valued matrix variable, we extend the recently developed successive pseudo-convex approximation framework and propose a novel iterative...
The most widely used method applied in the context of off-line dynamic demand calibration is Simultaneous Perturbation Stochastic Approximation (SPSA). In the research following the SPSA approach single origin-destination (O-D) demand components were mostly considered as calibration parameters. However, basic SPSA, especially in high dimensions, shows convergence issues, as proven by various authors...
The estimation of Origin — Destination (OD) matrix is a methodologically and computationally challenging, yet essential step in setting up a transportation planning model. In this process, demand (and supply) parameters need to be calibrated to match the simulation output with real observations. In this paper, we investigate how information extracted from a Jacobian matrix can be applied to improve...
In the case of particle swarm optimization, this paper mainly analyzes and discusses the mathematical model and the analysis on searching for the global optimum region. Firstly, the global optimum region Θ is defined and calculated in the convergence step and the divergence step. Furthermore, the rate μ of locating into the global optimum region is mathematically related to the number of particles,...
In the future, mixed AC and DC grids, spanning multiple areas operated by different transmission system operators (TSO), are expected to offer the necessary controllability for integrating large amounts of intermittent renewable generation. This is facilitated by high voltage direct current transmission based on voltage source converter technology that can offer recourse actions in the form of preventive...
In this contribution, an efficient Real-time Optimization (RTO) scheme for the optimal operation of chemical processes under uncertainty is proposed. This work builds on two recently published iterative robust optimization methodologies: Modifier Adaptation with Quadratic Approximation (MAWQA) and Directional Modifier Adaptation (DMA) and proposes a unified framework where the benefits of both methods...
We propose a novel approach to solve the many objective optimization (MaOO) problem using a ranking policy, instead of the Pareto ranking, supposing that a solution is unlikely to perform well for all objectives in a MaOO problem. A solution is thus evolved with respect to a specific objective only, which it may proficiently optimize. First, all objectives of the MaOO problem are individually optimized...
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