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Abstract This paper proposes a novel decomposition method based on user-preference and developed a variation of the decomposition based multi-objective optimization algorithm (MOEA/D) targeting only solutions in a small region of the Pareto-front defined by the preference information supplied by the decision maker (DM). This is particularly advantageous for solving multi-objective optimization problems (MOPs) with more than 3 objectives, i.e., many-objective optimization problems (MaOPs). As the number of objectives increases, the ability of an EMO algorithm to approximate the entire Pareto front (PF) is rapidly diminishing. In this paper, we first propose a novel scalarizing function making use of a series of new reference points derived from a reference point specified by the DM in the preference model. Based on this scalarizing function, we then develop a user-preference-based EMO algorithm, namely R-MOEA/D. One key merit of R-MOEA/D is that it does not rely on an estimation of the ideal point, which may impact significantly the performances of state-of-the-art decomposition based EMO algorithms. Our experimental results on multi-objective and many-objective benchmark problems have shown that R-MOEA/D provides a more direct and efficient search towards the preferred PF region, resulting in competitive performances. In an interactive setting when the DM changes the reference point during optimization, R-MOEA/D has a faster response speed and performance than the compared algorithms, showing its robustness and adaptability to changes of the preference model. Furthermore, the effectiveness of R-MOEA/D is verified on a real-world problem of reservoir flood control operations. [ABSTRACT FROM AUTHOR]

In the last decades, heuristic algorithms are widely used in solving problems in different fields of science and engineering. Most of these methods are inspired by natural phenomena, such as biological behaviours or physical principles. In this paper, a new optimization method based on the dynamic of sliding motion along a frictionless inclined plane is introduced. In the proposed algorithm, a collection of agents cooperate with each other and move toward better positions in the search space by employing Newton's second law and equations of motion. Our method is compared with other popular optimization algorithms and the results on 23 standard benchmark functions show its effectiveness in most cases. [ABSTRACT FROM AUTHOR]

In this paper, a Hierarchical Trust Region Algorithm for solving PDE-constrained optimization problems is developed. A hierarchy of finite element meshes is used to define a hierarchy of quadratic models for the approximation of the discrete reduced cost functional on the finest mesh. The proposed algorithm simultaneously controls the choice of the model and the size of the trust region radius. Application of the trust region convergence theory allows for proving that every accumulation point of the sequence produced by the algorithm is a stationary point of the discretized problem. Numerical examples illustrate the behavior of the method and show a considerable reduction of computation time compared to the standard Newton trust region scheme. [ABSTRACT FROM AUTHOR]

This paper focuses on the three dimensional flight path planning for a UAV on a low altitude terrain following/terrain avoidance mission. Using an approximate grid-based discretisation scheme, we transform the continuous optimisation problem into a search problem over a finite network, and apply a variant of the shortest-path algorithm to this problem. In other words using the three dimensional terrain information, three dimensional flight path from a starting point to an end point, minimising a cost function and regarding the kinematics constraints of the UAV is calculated. A network flow model is constructed based on the digital terrain elevation data (DTED) and a layered network is obtained. The cost function for each arc is defined as the length of the arc, then a constrained shortest path algorithm which considers the kinematics and the altitude constraints of the UAV is used to obtain the best route. Moreover the important performance parameters of the UAV are discussed. Finally a new algorithm is proposed to smooth the path din order to reduce the workload of the autopilot and control system of the UAV. The numeric results are presented to verify the capability of the procedure to generate admissible route in minimum possible time in comparison to the previous procedures. So this algorithm is potentially suited for using in online systems. [ABSTRACT FROM AUTHOR]

This paper proposes a parallel optimization algorithm PDF/IHS for the minimum execution-time multi- processor scheduling problem which is a strong NP-hard optimization problem. PDF/IHS is a parallelization and efficient implementation of the only practical optimization algorithm DFIIHS among those which have been proposed for this scheduling problem. In PDF/IHS, processors perform depth-first search in parallel on a heuristically generated search tree in such a way that it is searched hierarchically from the left- and right-band sides. The effectiveness of PDF/IHS has been verified by simulation and practical parallel processing on Alliant FX4. As a result, it has been recognized that most of the problems which required a long time by DF/IHS can be solved approximately in time by PDF/IHS using m processors. Moreover, even for a problem which required a very long time or could not be solved in a practical time by DF/IHS, it has been verified that PDF/ IHS can give solutions in time less than 1/m. [ABSTRACT FROM AUTHOR]

In this paper, we aim to formulate the recognition of a linear manifold from a discrete point set as a nonlinear optimization problem, and we prove a uniqueness theorem for the solution of this problem. We also develop an algorithm for the computation of parameters of the Euclidean linear manifold from pixels and voxels on a plane and in a space, respectively. [Copyright &y& Elsevier]