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This paper addresses the reconstruction issue of sparse signal by developing centralized and distributed neurodynamic approaches. An l 1 -minimization problem can be employed for reconstructing sparse signal, and for solving this problem, a centralized neurodynamic approach with derivative feedback is designed. Considering the fact that the distributed approaches decompose the large-scale problem into small-scale one without central processing node in centralized ones, it effectively reduces the computational complexity of a single node. According to the distributed consensus and graph theory, the original l 1 -minimization problem can be equivalently transformed as a distributed optimization problem. Then, based on our proposed centralized approach, a distributed neurodynamic approach with derivative feedback is further proposed. Through the convex optimization theory and Lyapunov method, we indicate that the optimal solution of l 1 -minimization problem is equivalent to the equilibrium point of centralized or distributed approach, and that each neurodynamic approach globally converges to its equilibrium point. Furthermore, by comparing with several state-of-the-art neurodynamic approaches, our proposed approaches demonstrate their effectiveness and superiority by simulation results in reconstructing sparse signals and images. [ABSTRACT FROM AUTHOR]

Recently, numerous meta-heuristic-based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity, etc. However, several optimization algorithms namely firefly algorithm, sine–cosine algorithm, and particle swarm optimization algorithm have few drawbacks such as computational complexity and convergence speed. So to overcome such shortcomings, this paper aims in developing a novel chaotic sine–cosine firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine–cosine algorithm and the firefly algorithms is integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping are selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an efficient heuristic method to minimize total flow time in no-wait flow shop scheduling. It is based on the assumption that the priority of a job in the initial sequence is given by the sum of its processing times on the bottleneck machines. Empirical results demonstrate the superiority of the proposed method over the best-known heuristics in the literature, while remaining the same complexity order of O( n2). [ABSTRACT FROM AUTHOR]

In this paper, the berth allocation problem with stochastic vessel handling times is formulated as a bi-objective problem. To solve the resulting problem, an evolutionary algorithm-based heuristic and a simulation-based Pareto front pruning algorithm is proposed. Computational examples show that the proposed approach provides solutions superior to the ones where the expected value of the vessel handling times is used. [ABSTRACT FROM AUTHOR]

We consider bilevel optimization from the optimistic point of view. Let the pair ( x, y) denote the variables. The main difficulty in studying such problems lies in the fact that the lower level contains a global constraint. In fact, a point ( x, y) is feasible if y solves a parametric optimization problem L( x). In this paper we restrict ourselves to the special case that the variable x is one-dimensional. We describe the generic structure of the feasible set M. Moreover, we discuss local reductions of the bilevel problem as well as corresponding optimality criteria. Finally, we point out typical problems that appear when trying to extend the ideas to higher dimensional x-dimensions. This will clarify the high intrinsic complexity of the general generic structure of the feasible set M and corresponding optimality conditions for the bilevel problem U. [ABSTRACT FROM AUTHOR]