Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the
goal of automating the design of heuristic methods to solve hard computational search …

In this paper, we introduce a multi-objective selection hyper-heuristic approach combining
Reinforcement Learning,(meta) heuristic selection, and group decision-making as …

In this paper, local search algorithms are proposed for the p-Center, α-Neighbour p-Center
and p-Next Center facility location problems. The α-Neighbour p-Center and p-Next Center …

Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good
performance in solving different optimization problems. However, PSO usually suffers from …

Many techniques that boost the speed or quality of metaheuristic search have been reported
within literature. The present contribution investigates the rather rare combination of …

Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms
and have been successfully applied to a range of computationally hard real-world problems …

A hyper-heuristic performs search over a set of other search mechanisms. During the
search, it does not require any problem-dependent data. This structure makes hyper …

This study provides a new hyper-heuristic design using a learning-based heuristic selection
mechanism together with an adaptive move acceptance criterion. The selection process was …

In the present study, a large scale, structured problem regarding the routing and rostering of
security personnel is investigated. Structured problems are combinatorial optimization …

The existence of several solution strategies for different search and optimisation problems
motivates the use of these strategies in a collaborative way. These collaborative approaches …