A comparison of semi-deterministic and stochastic search techniques

TL;DR

This paper compares semi-deterministic tabu search and stochastic simulated annealing for engineering design optimization, evaluating their effectiveness on complex, multi-modal problems against a steepest descent method.

Contribution

It provides a comparative analysis of TS and SA, highlighting their relative strengths and weaknesses in solving multi-modal engineering optimization problems.

Findings

Tabu search effectively escapes local optima using semi-deterministic strategies.

Simulated annealing demonstrates robust global search capabilities with stochastic methods.

Both techniques outperform steepest descent in complex, multi-modal problems.

Abstract

This paper presents an investigation of two search techniques, tabu search (TS) and simulated annealing (SA), to assess their relative merits when applied to engineering design optimisation. Design optimisation problems are generally characterised as having multi-modal search spaces and discontinuities making global optimisation techniques beneficial. Both techniques claim to be capable of locating globally optimum solutions on a range of problems but this capability is derived from different underlying philosophies. While tabu search uses a semi-deterministic approach to escape local optima, simulated annealing uses a complete stochastic approach. The performance of each technique is investigated using a structural optimisation problem. These performances are then compared to each other as and to a steepest descent (SD) method.