Evolutionary algorithms, simulated annealing and tabu search: a comparative study

Evolutionary algorithms, simulated annealing (SA), and tabu search (TS) are general iterative algorithms for combinatorial optimization. The term evol utionary algorithm is used to refer to any probabilistic algorithm whose de sign is inspired by evolutionary mechanisms found in biological species. Mo st widely known algorithms of this category are genetic algorithms (GA). GA ? SA, and TS have been found to be very effective and robust in solving num erous problems from a wide range of application domains. Furthermore, they are even suitable for ill-posed problems where some of the parameters are n ot known before hand. These properties are lacking in all traditional optim ization techniques. In this paper we perform a comparative study among GA, SA, and TS. These algorithms have many similarities, but they also possess distinctive features, mainly in their strategies for searching the solution state space. The three heuristics are applied on the same optimization pro blem and compared with respect to (1) quality of the best solution identifi ed by each heuristic, (2) progress of the search from initial solution(s) u ntil stopping criteria are met: (3) the progress of the cost of the best so lution as a function of time (iteration count), and (4) the number of solut ions found at successive intervals of the cost function. The benchmark prob lem used is the floorplanning of very large scale integrated (VLSI) circuit s. This is a hard multi-criteria optimization problem. Fuzzy logic is used to combine all objective criteria into a single fuzzy evaluation (cost) fun ction, which is then used to rate competing solutions. (C) 2001 Elsevier Sc ience Ltd. All rights reserved.