Hyper-heuristics have emerged as a way to raise the level of generality of search techniques
for computational search problems. This is in contrast to many approaches, which represent …

Abstract Travelling Salesman Problem (TSP) is an Np-Hard problem, for which various
solutions have been offered so far. Using the Harris Hawk Optimization (HHO) algorithm, this …

This paper introduces a new and very simple search methodology called Late Acceptance
Hill-Climbing (LAHC). It is a local search algorithm, which accepts non-improving moves …

Abstract The Artificial Bee Colony (ABC) algorithm is a swarm intelligence approach which
has initially been proposed to solve optimisation of mathematical test functions with a unique …

Hyper-heuristic is a class of methodologies which automates the process of selecting or
generating a set of heuristics to solve various optimization problems. A traditional hyper …

Efficient truck dispatching is crucial for optimizing container terminal operations within
dynamic and complex scenarios. Despite good progress being made recently with more …

This paper proposes a novel surrogate ensemble-assisted hyper-heuristic algorithm (SEA-
HHA) to solve expensive optimization problems (EOPs). A representative HHA consists of …

Capacity limitations, combined with increased air-traffic, continue to drive the need for better
resource management at airports. At congested airports, the allocation of resources for …

This paper proposes a novel hyper-heuristic algorithm termed evolutionary multi-mode slime
mold optimization (EMSMO) for addressing continuous optimization problems. The …

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