• An inequality constrained optimization problem is an optimization problem in which the constraint set D can be represented as D = U ∩ {x ∈ R | h(x) ≥ 0}, where h : R → R. We refer to the functions h = (h1, . . . , hl) as inequality constraints. • An optimization problem with mixed constraints is an optimization problem in which the constraint set D can be represented as D = U ∩ {x ∈ R | g(x) ...

Constraint Cellular ant algorithm is a new optimization method for solving real problems by using both constraints method, the evolutionary rule of cellular, graph theory and the characteristics of ant colony optimization. Multi-objective vehicle routing problem is very important and practical in logistic research fields, but it is difficult to model and solve because objectives have complicate...

The use of constraint optimization has recently proven to be a successful approach to providing solutions to various NP-hard search and optimization problems in data analysis. In this work we extend the use of constraint optimization systems further within data analysis to a central problem arising from the analysis of multivariate data, namely, determining minimum-width multivariate confidence...

Based on the combination of the particle swarm algorithm and multiplier penalty function method for the constraint conditions, this paper proposes an improved hybrid particle swarm optimization algorithm which is used to solve nonlinear constraint optimization problems. The algorithm converts nonlinear constraint function into no-constraints nonlinear problems by constructing the multiplier pen...

In constraint optimization, global constraints play a decisive role. To develop an efficient optimization tool, we need to be able to assess whether we are still able to improve the objective function further. This observation has lead to the development of a special kind of global constraints, so-called optimization constraints [2, 5]. Roughly speaking, an optimization constraint expresses our...

Constraint Programming (CP) solvers typically tackle optimization problems by repeatedly finding solutions to a problem while placing tighter and tighter bounds on the solution cost. This approach is somewhat naive, especially for soft-constraint optimization problems in which the soft constraints are mostly satisfied. Unsatisfiable-core approaches to solving soft constraint problems in SAT (e....

In recent years, it has been increasingly recognized that constraint and integer programming have orthogonal and complementary strengths in stating and solving combinatorial optimization applications. In addition, their integration has become an active research topic. The optimization programming language opl was a first attempt at integrating these technologies both at the language and at the ...

A study of the stability properties of the set of equilibrium points that are solutions for a given constraint satisfaction or optimization problem mapped to Hopfield network dynamics is presented. Specifically, the relation between constraint weight parameter values and the stability of solutions of optimization problems mapped to Hopfield networks is investigated. A theoretical development re...

One of the limitation of the constraint network formalism lies in its inability of explicitly expressing a criteria to optimize. The introduction of several ad-hoc optimization mechanisms in constraint (logic) programming languages shows how important this restriction is. Several formalisms of varied generality have been proposed to remove this restriction: fuzzy constraint networks, partial co...

Helios is the first (to our knowledge) modeling language for global optimization using interval analysis. Helios makes it possible to state global optimization problems almost as in scientific papers and textbooks and is guaranteed to find all isolated solutions in constraint-solving problems and all global optima in optimization problems. Helios statements are compiled to Newton, a constraint ...