Ant colony optimization (ACO) is a stochastic search method for solving NP-hard problems. The exploration versus exploitation dilemma rises in ACO search. Reactive max-min ant system algorithm is a recent proposition to automate the exploration and exploitation. It memorizes the search regions in terms of reactive heuristics to be harnessed after restart, which is to avoid the arbitrary explora...

Vendor selection decisions are complicated by the fact that various conflicting multi-objective factors must be considered in the decision making process. The problem of vendor selection becomes still more compli-cated with the inclusion of incremental discount pricing schedule. Such hard combinatorial problems when solved using meta heuristics produce near optimal solutions. This paper propose...

The cross-entropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based on Kullback–Leibler (or cross-entropy) minimization. As an optimization method it unifies many existing populationbased optimization heuristics. In this chapter we show how the cross-entropy method can be applied to a diverse range of combinatorial, continuous, and noisy opti...

vendor selection decisions are complicated by the fact that various conflicting multi-objective factors must be considered in the decision making process. the problem of vendor selection becomes still more compli-cated with the inclusion of incremental discount pricing schedule. such hard combinatorial problems when solved using meta heuristics produce near optimal solutions. this paper propose...

This paper introduces a new self-tuning mechanism to the local search heuristic for solving of combinatorial optimization problems. Parameter tuning of heuristics makes them difficult to apply, as parameter tuning itself is an optimization problem. For this purpose, a modified local search algorithm free from parameter tuning, called SelfAdaptive Local Search (SALS), is proposed for obtaining q...

Human performance on instances of computationally intractable optimization problems, such as the travelling salesperson problem (TSP), can be excellent. We have proposed a boundary-following heuristic to account for this finding. We report three experiments with TSPs where the capacity to employ this heuristic was varied. In Experiment 1, participants free to use the heuristic produced solution...

Nodifferencewas detected on the performances of construction heuristics for TSP developed in Task 1 between Euclidean instances with points uniformly sparse and Euclidean instances with points clustered. Attention was given to the choice of data structures to represent and maintain a solution during a perturbative search. We described Tabu Search (TS) and its variations: robust and reactive. We...

In this paper we try to describe the main characters of Heuristics \derived" from Nature, a border area between Operations Research and Arti cial Intelligence, with applications to graph optimization problems. These algorithms take inspiration from physics, biology, social sciences, and use a certain amount of repeated trials, given by one or more \agents" operating with a mechanism of competit...

In this paper, we propose mechanisms to improve instantiation heuristics by incorporating weighted factors on variables. The proposed weight-based heuristics are evaluated on several tree search methods such as chronological backtracking and discrepancy-based search for both constraint satisfaction and optimization problems. Experiments are carried out on random constraint satisfaction problems...