Surrogate Branching (SB) methods in mixed integer optimization provide a staged parametric relaxation of customary branching methods used in branch-and-bound and branch-and-cut algorithms. SB methods operate by forming surrogate constraints composed of non-negative linear combinations of component inequalities of three types: (1) ordinary branching inequalities, (2) redundant inequalities invol...

A transactional agent is a mobile agent which manipulates objects in one or more than one computer so as to satisfy some constraint like ACID. An agent creates a surrogate agent on a computer on leaving the computer. A surrogate holds objects manipulated by the agent until the agent terminates. The surrogate can recreate a new incarnation of the agent if the agent is faulty. Transactional agent...

We propose a Markov chain method to efficiently generate surrogate networks that are random under the constraint of given vertex strengths. With these strength-preserving surrogates and with edge-weight-preserving surrogates we investigate the clustering coefficient and the average shortest path length of functional networks of the human brain as well as of the International Trade Networks. We ...

Optimization Involving Expensive Black-Box Objective and Constraint Functions Rommel G. Regis Mathematics Department, Saint Joseph’s University, Philadelphia, PA 19131, USA, [email protected] August 23, 2010 Abstract. This paper presents a new algorithm for derivative-free optimization of expensive black-box objective functions subject to expensive black-box inequality constraints. The proposed al...

The multidimensional multi-choice knapsack problem (MMKP) is one of the most complex members of the Knapsack Problem (KP) family. It has been used to model large problems such as telecommunications, quality of service (QoS), management problem in computer networks and admission control problem in the adaptive multimedia systems. In this paper, we propose a new approach based on strategic oscill...

Surrogate-assisted evolutionary multiobjective optimization algorithms are often used to solve computationally expensive problems. But their efficacy on handling constrained optimization problems having more than three objectives has not been widely studied. Particularly the issue of how feasible and infeasible solutions are handled in generating a data set for training a surrogate has not rece...

Many engineering problems require to optimize the system performance subject to reliability constraints, and this type of problems are commonly referred to as the reliability based optimization (RBO) problems. In this work we propose a derivativefree trust-region (DF-TR) based algorithm to solve the RBO problems. In particular, we are focused on the type of RBO problems where the objective func...

This paper proposes a class of surrogate constraint heuristics for obtaining approximate, 'near optimal solutions to integer programming problems. These heuris· tics are based on a simple framework that illuminates the character of several carlier heuristic proposals and provides a variety of new alternatives. The paper also pro· poses additional heuristics that can be used either to supplement...

The set covering problem (SCP) is central in a wide variety of practical applications for which finding good feasible solutions quickly (often in real-time) is crucial. Surrogate constraints normalization is a classical technique used to derive appropriate weights for surrogate constraint relaxations in mathematical programming. This framework remains the core of the most effective one-pass con...

This paper discusses the benefits of different infill sampling criteria used in surrogate-model-based constrained global optimization. Here surrogate models are used to approximate both the objective and constraint functions with the assumption that these are computationally expensive to compute. The construction of these surrogates (also known as meta models or response surface models) involve...