An Ant-Based Selection Hyper-heuristic for Dynamic Environments

Abstract

Abstract. Dynamic environment problems require adaptive solution methodologies which can deal with the changes in the environment during the solution process for a given problem. A selection hyper-heuristic manages a set of low level heuristics (operators) and decides which one to apply at each iterative step. Recent studies show that selection hyperheuristic methodologies are indeed suitable for solving dynamic environment problems with their ability of tracking the change dynamics in a given environment. The choice function based selection hyper-heuristic is reported to be the best hyper-heuristic on a set of benchmark problems. In this study, we investigate the performance of a new learning hyper-heuristic and its variants which are inspired from the ant colony optimisation algorithm components. The proposed hyper-heuristic maintains a matrix of pheromone intensities (utility values) between all pairs of low level heuristics. A heuristic is selected based on the utility values between the previously invoked heuristic and each heuristic from the set of low level heuristics. The ant-based hyper-heuristic performs better than the choice function and even its improved version across a variety of dynamic environments produced by the Moving Peaks Benchmark generator.

Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery

“…After recent application of the LLHs, the obtained solution is considered for accepting as incumbent solution into next iteration. If the new solution is at least as good as the previous solution it will replace, and then it is automatically accepted as current solution regardless of the nature of an acceptance mechanism; otherwise, acceptance criterion is utilized to whether discard the 6 Complexity new solution or not [65,84]. e following four acceptance criteria are proposed to pair up with the above selection strategies, aiming at pointing out which pairs perform better than others.…”

“…× G best (t), (27) where t and t max indicate the current steps and the maximum iteration and G best (t) represents the global best solution objective value found so far. (4) SA [65]: new solutions are accepted as the current solutions if the probability criterion is met, that is, improving results are accepted with probability value at 1 while no-improving results are accepted if a uniform value within [0, 1] is less the critical value. e probability of accepting a no-improving solution in SA is calculated for making a decision on whether accepting it or not [65]:…”

Analysis of global information sharing in hyper-heuristics for different dynamic environments

“…The EDA was used in the on-line phase to guide the search to promising areas. • Kiraz et al used an ant-based selection hyper-heuristic that use pheromone intensities between all possible pairings of heuristics to guide the heuristic ordering choice [12]. Simple Gaussian mutation operators were used as heuristics, the moving peaks benchmark was used, and only spatial and temporal changes were considered.…”

“…The intuitive expectation is that hyper-heuristics should be well-suited to solve dynamic optimisation problems because different heuristic search methods can be brought to bear at different times during the search. Recently a number of studies have investigated this expectation [9] [10] [11] [12] [13] [14].…”

A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex

“…Placing a heavy emphasis on the intensification component of the original Choice Function, the Modified Choice Function showed increased performance on average over the CHeSC2011 benchmark instances. Modified Choice Function heuristic selection has also been used in the context of dynamic environments [18] and the multidimensional knapsack problem [12].…”