Evolutionary Algorithm for Chance Constrained Quadratic Multiple Knapsack Problem

TL;DR

This paper introduces a hybrid evolutionary algorithm combined with local optimisation based on multi-factorial optimisation to solve a stochastic quadratic multiple knapsack problem with chance constraints, demonstrating improved performance on complex resource allocation scenarios.

Contribution

It proposes a novel hybrid approach integrating EAs with MFO-inspired local search for stochastic QMKP, addressing the challenge of randomness in profits and complex constraints.

Findings

Hybrid methods outperform standalone EAs in tight constraint scenarios

MFO-based local optimisation enhances solution quality

Hybrid approach effectively handles stochastic profit variability

Abstract

Quadratic multiple knapsack problem (QMKP) is a combinatorial optimisation problem characterised by multiple weight capacity constraints and a profit function that combines linear and quadratic profits. We study a stochastic variant of this problem where profits are considered as random variables. This problem reflects complex resource allocation problems in real-world scenarios where randomness is inherent. We model this problem using chance constraints to capture the stochastic profits. We propose a hybrid approach for this problem, which combines an evolutionary algorithm (EA) with a local optimisation strategy inspired by multi-factorial optimisation (MFO). EAs are used for global search due to their effectiveness in handling large, complex solution spaces. In the hybrid approach, EA periodically passes interim solutions to the local optimiser for refinement. The local optimiser applies MFO principles, which are typically used in multi-tasking problems. The local optimiser models the local problem as a multi-tasking problem by constructing disjoint search spaces for each knapsack based on an input solution. For each item, its assignment across all knapsacks is considered to determine the preferred knapsack. Items are then divided into disjoint groups corresponding to each knapsack, allowing each knapsack to be treated as a separate optimisation task. This structure enables effective application of MFO-based local refinements. We consider two EAs for the problem, (1+1) EA and ($\mu+\lambda$) EA. We conduct experiments to explore the effectiveness of these EAs on their own and also with the proposed local optimiser. Experimental results suggest that hybrid approaches, particularly those incorporating MFO, perform well on instances where chance constraints and capacity constraints are tight.