Comparison of Metaheuristics

Metaheuristics are truly diverse in nature — under the overarching theme of performing operations to escape local optima (we assume minima in this chapter without loss of generality), algorithms as different as ant colony optimization ([12]), tabu search ([16]), and genetic algorithms ([23]) have emerged. Due to the unique functionality of each type of metaheuristic, comparison of metaheuristics is in many ways more difficult than other algorithmic comparisons. In this chapter, we discuss techniques for meaningful comparison of metaheuristics. In Section 2, we discuss how to create and classify instances in a new testbed and how to make sure other researchers have access to the problems for future metaheuristic comparisons. In Section 3, we discuss the disadvantages of large parameter sets and how to measure complicating parameter interactions in a metaheuristic’s parameter space. Last, in Sections 4 and 5, we discuss how to compare metaheuristics in terms of both solution quality and runtime.