Abstract This paper discusses an active set trust-region algorithm for bound-constrained optimization problems. A sufficient descent condition is used as a computational measure to identify whether the function value reduced or not. To get our complexity result, critical which computationally better than other known measures. Under positive definiteness of approximated Hessian matrices restrict...

We propose a new algorithm for solving Distributed Constraint Optimization Problems (DCOPs). Our algorithm, called DyBop, is based on branch and bound search with dynamic ordering of agents. A distinctive feature of this algorithm is that it uses the concept of valued nogood. Combining lower bounds on inferred valued nogoods computed cooperatively helps pruning dynamically unfeasible sub-proble...

Distributed constraint optimization problems enable the representation of many combinatorial problems that are distributed by nature. An important motivation for such problems is to preserve the privacy of the participating agents during the solving process. The present paper introduces a novel privacy-preserving branch and bound algorithm for this purpose. The proposed algorithm, P-SyncBB, pre...

Robust optimization can provide safe and tractable analytical approximation for the chance constrained optimization problem. In this work, we studied the application of robust optimization approximation in solving chance constrained planning and scheduling problem under uncertainty. Four different robust optimization approximation methods for improving the quality of robust solution were invest...

Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, evolutionary algorithms (EAs) were developed still are developing under umbrella of computation. general, EAs mainly categorized into nature-inspired swarm-intelligence- (SI-) based paradigms. All these have some merits also demerits. Particle swarm (PS...

we establish a relationship between general constrained pseudoconvex optimization problems and globally projected dynamical systems. a corresponding novel neural network model, which is globally convergent and stable in the sense of lyapunov, is proposed. both theoretical and numerical approaches are considered. numerical simulations for three constrained nonlinear optimization problems a...

In this paper we consider the non-preemptive variant of a multi-mode resource constrained project scheduling problem (MRCPSP) with mode identity, in which a set of project activities is partitioned into disjoint subsets while all activities forming one subset have to be processed in the same mode. We present a depth-first branch and bound algorithm for the resource constrained project schedulin...