Greedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimum labelling spanning tree problem

This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-hard. A Greedy Randomized Adaptive Search Procedure (GRASP) and a Variable Neighbourhood Search (VNS) are proposed in this paper. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics

The Traveling Salesman Problem in the Natural Environment

Is it possible for humans to navigate in the natural environment wherein the path taken between various destinations is 'optimal' in some way? In the domain of optimization this challenge is traditionally framed as the "Traveling Salesman Problem" (TSP). What strategies and ecological considerations are plausible for human navigation? When given a two-dimensional map-like presentation of the destinations, participants solve this optimization exceptionally well (only 2-3% longer than optimum)^1, 2^. In the following experiments we investigate the effect of effort and its environmental affordance on navigation decisions when humans solve the TSP in the natural environment. Fifteen locations were marked on two outdoor landscapes with flat and varied terrains respectively. Performance in the flat-field condition was excellent (∼6% error) and was worse but still quite good in the variable-terrain condition (∼20% error), suggesting participants do not globally pre-plan routes but rather develop them on the fly. We suggest that perceived effort guides participant solutions due to the dynamic constraints of effortful locomotion and obstacle avoidance

Extending the Bounded Rationality Model: The Distributed Cognition Approach

The way Simon, and the major part of the scholars, presented and used bounded rationality directly refers to human computational capabilities (or “brute-force”). Despite its broad powers of explanation, some problems arise when taking into account the way the human cognitive system really works. In order to avoid these problems, we present an alternative model of rationality, where computation plays only a part, together with the implemented role of external resources, emotional and other non-strictly-rational variables.bounded rationality, distributed cognition, external resources, decision-making, problem solving, emotions