Ant Colony Optimization Parameters Control Based on Evolutionary Strength Ant Colony Optimization (ACO) was developed to be a metaheuristic and widely used for combinatorial optimization problems. It is confirmed that the parameters setting is of vital importance for performance of ACO. This paper proposed a strategy to perform ACO Parameters Control based on Evolutionary Strength (ACOP_ES). First, we transform four parameters setting problem to decision making problem. Then, by learning the evolutionary strength curve, we set sum, peak, and kurtosis of evolutionary strength curve as the objective function, such that the continuous ant colony algorithm can be used to optimize this decision problem. Further, via characterizing an excellent evolutionary curve, a relatively optimal parameters setting can be found. Compared with existing methods, our proposed method focuses on the performance of the running process rather than the results only. To the best of our knowledge, we are among the first to introduce process performance evaluation of ACO to guide parameters control, and determine the relatively optimal parameters of ACO. At last, we apply our method to Multidimensional Knapsack Problem (MKP), the results look promising that parameters returned by ACOP_ES are better than the ones returned by other methods on datasets: WEISH*.DAT and WEING*.DAT.

Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem Research Frontier Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporationbased and populationbased. A case study of using these algorithms to solve the dynamic traveling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.