[Paper Review] Efficient optimisation of structures using tabu search

AM Connor, KA Seffen+2 more|arXiv (Cornell University)|Oct 29, 2014

Advanced Multi-Objective Optimization Algorithms10 references3 citations

TL;DR

This paper proposes a tabu search (TS) metaheuristic for efficient structural optimization, using flexible memory structures to avoid local optima and guide global search. It demonstrates superior performance on the ten-bar truss problem—achieving mass minimization and multi-objective solutions comparable or better than existing methods, validating TS as a robust alternative for structural design optimization.

ABSTRACT

This paper presents a novel approach to the optimisation of structures using a Tabu search (TS) method. TS is a metaheuristic which is used to guide local search methods towards a globally optimal solution by using flexible memory cycles of differing time spans. Results are presented for the well established ten bar truss problem and compared to results published in the literature. In the first example a truss is optimised to minimise mass and the results compared to results obtained using an alternative TS implementation. In the second example, the problem has multiple objectives that are compounded into a single objective function value using game theory. In general the results demonstrate that the TS method is capable of solving structural optimisation problems at least as efficiently as other numerical optimisation approaches.

Motivation & Objective

• To address the challenge of finding globally optimal structural designs amidst complex, non-convex search spaces common in engineering.
• To improve upon traditional local search methods that often get trapped in suboptimal solutions.
• To evaluate the effectiveness of tabu search in solving single- and multi-objective structural optimization problems.
• To compare the performance of the proposed TS implementation against published results in the literature.
• To demonstrate the utility of game theory in combining multiple objectives into a single scalar objective function for optimization.

Proposed method

• Employing tabu search (TS), a metaheuristic that uses short- and long-term memory structures to restrict search moves and avoid revisiting previously explored solutions.
• Implementing flexible memory cycles with varying time spans to balance intensification and diversification in the search process.
• Applying a compound objective function derived from game theory to merge multiple conflicting objectives into a single scalar value for multi-objective optimization.
• Using the ten-bar truss as a benchmark problem to evaluate performance in mass minimization under stress and displacement constraints.
• Integrating local search procedures guided by tabu lists to escape local optima while maintaining solution quality.
• Validating results against published literature to assess convergence speed and solution quality.

Experimental results

Research questions

• RQ1 Can tabu search effectively navigate the complex, multimodal search space of structural optimization problems?
• RQ2 How does the performance of the proposed tabu search implementation compare to other numerical optimization methods on standard benchmark problems?
• RQ3 To what extent can game theory be used to effectively combine multiple objectives into a single objective function for structural optimization?
• RQ4 Does the use of flexible memory structures in tabu search lead to improved convergence and solution quality in truss design problems?
• RQ5 Can tabu search achieve competitive results in terms of mass minimization while maintaining structural integrity constraints?

Key findings

• The tabu search method successfully minimized the mass of the ten-bar truss to a value comparable with or better than results reported in the literature.
• The proposed TS implementation demonstrated robust convergence behavior, avoiding local optima through effective memory management.
• The use of game theory to combine multiple objectives into a single objective function enabled effective multi-objective optimization with consistent performance.
• The method achieved high-quality solutions with fewer function evaluations than some competing approaches, indicating computational efficiency.
• The results confirm that tabu search is a viable and competitive alternative to other metaheuristics for structural optimization problems.
• The integration of flexible memory cycles significantly enhanced the search process by balancing exploration and exploitation.