Many problems in multi-agent systems can be represented as a Distributed Constraint Optimization Problem (DCOP) where the goal is to find the best assignment to variables in order to minimize the cost. More complex problems including several criteria can be represented as a Multi-Objective Distributed Constraint Optimization Problem (MODCOP) where the goal is to optimize several criteria at the...

Beetle antennae search (BAS) is an efficient metaheuristic algorithm inspired by foraging behaviors of beetles. This algorithm includes several parameters for tuning and the existing results are limited to solve single objective optimization. This work pushes forward the research on BAS by providing one variant that releases the tuning parameters and is able to handle multi-objective optimizati...

Because of their complementary strengths, optimization and constraint programming can be profitably merged. Their integration has been the subject of increasing commercial and research activity. This paper summarizes and contrasts the characteristics of the two fields; in particular, how they use logical inference in different ways, and how these ways can be combined. It sketches the intellectu...

This work is an example of application of nonlinear programming to a problem of three-dimensional trajectory optimization for multistage launch vehicles for geostationary orbit missions. The main objective is to minimize fuel consumption or equivalently to maximize the payload. The launch vehicle considered here, Europa-II, consists of 5 thrust phases and 2 coast phases. Major parameters of the...

Sequential optimality conditions provide adequate theoretical tools to justify stopping criteria for nonlinear programming solvers. Here, nonsmooth approximate gradient projection and complementary approximate Karush-Kuhn-Tucker conditions are presented. These sequential optimality conditions are satisfied by local minimizers of optimization problems independently of the fulfillment of constrai...

The pupose of this paper is modeling of Partial Digest Problem (PDP) as a mathematical programming problem. In this paper we present a new viewpoint of PDP. We formulate the PDP as a continuous optimization problem and develope a method to solve this problem. Finally we constract a linear programming model for the problem with an additional constraint. This later model can be solved by the simp...

The complexity of any optimisation problem depends critically on the form of the objective function. Valued constraint satisfaction problems are discrete optimisation problems where the function to be minimised is given as a sum of cost functions defined on specified subsets of variables. These cost functions are chosen from some fixed set of available cost functions, known as a valued constrai...

Scheduling a subset of solvers belonging to a given portfolio has proven to be a good strategy when solving Constraint Satisfaction Problems (CSPs). In this paper, we show that this approach can also be effective for Constraint Optimization Problems (COPs). Unlike CSPs, sequential execution of optimization solvers can communicate information in the form of bounds to improve the performance of t...

In classical planning, the planner is given a concrete goal; it returns a plan for it or a failure message. In the latter case, the user can either quit or modify the goal. For many applications, it is more convenient to let the user provide a more elaborate specification consisting of constraints and preferences over possible goal states. Then, let the system discover a plan for the most desir...

Complete algorithms for solving DisCOPs have been a major focus of research in the DCR community in the last few years. The properties of these algorithms belong to very different categories. Algorithms differ by their degree of asynchronicity, by the method of their combinatorial part, and by dividing the problem into sub parts. The wide variety of different families of algorithms makes it har...