Evolutionary algorithms, simulated annealing (SA), and Tabu Search (TS) are general iterative algorithms for combinatorial optimization. The term evolutionary algorithm is used to refer to any probabilistic algorithm whose design is inspired by evolutionary mechanisms found in biological species. Most 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 numerous problems from a wide range of application domains.Furthermore, they are even suitable for ill-posed problems where some of the parameters are not known before hand. These properties are lacking in all traditional optimization 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 problem and compared with respect to (1) quality of the best solution identified by each heuristic, (2) progress of the search from initial solution(s) until stopping criteria are met, (3) the progress of the cost of the best solution as a function of time, and (4) the number of solutions found at successive intervals of the cost function. The benchmark problem was is the floorplanning of very large scale integrated circuits. This is a hard multi-criteria optimization problem. Fuzzy logic is used to combine all objective criteria into a single fuzzy evaluation function, which is then used to rate competing solutions.
Genetic algorithms (GAs) have been found to be very effective in solving numerous optimization problems, especially those with many (possibly) conflicting and noisy objectives. However, there seems to be no consensus as to what fitness measure to use in such situations, and how to rank individuals in a population on the basis of several conflicting objectives. Fuzzy logic provides an effective and easy way of dealing with such class of problems. In this work, we present a fuzzy genetic algorithm (FGA), which combines the parallel and robust search properties of GA with the expressive power of fuzzy logic. In the proposed FGA, the fitness of individuals is evaluated based on fuzzy logic rules expressed on linguistic variables modeling the desired objective criteria of the problem domain. FGA is compared to a weighted sum GA (WS-GA) where the fitness is set equal to a weighted sum of the objective criteria. Also, sever fitness fuzzification approaches are evaluated. Experimental evaluation was conducted using as a testbed the floorplanning of very large scale integrated (VLSI) circuits.
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