Quantum-inspired optimisation algorithms represent a class of metaheuristic techniques that draw inspiration from quantum mechanics to tackle combinatorial optimisation problems on classical hardware.

Two-dimensional (2D) irregular packing problems are widespread in manufacturing industries such as shipbuilding, metalworking, automotive production, aerospace, clothing and furniture manufacturing.

A major barrier to the wider use of supervised learning in emerging applications, such as genomic selection, is the lack of sufficient and representative labeled data to train prediction models. The ...

Implementation of simulated annealing algorithm for the multiple choice multidimensional knapsack problem. The multiple choice knapsack problem has n groups of items and m constraints. The objective ...

[Ahuja00] “A greedy genetic algorithm for the quadratic assignment problem”, R. Ahuja, J. Orlin, A. Tiwari, Computers and Operations Research, vol. 27, issue 10 (Sept. 2000), 917--934, ACM (2000) ...

Abstract: In this paper, we present some initial results of several meta-heuristic optimization algorithms, namely, genetic algorithms, simulated annealing, branch and bound, dynamic programming, ...

This paper presents a continuous method for solving binary quadratic programming problems. First, the original problem is converted into an equivalent continuous optimization problem by using NCP ...

Abstract: Knapsack problem is a traditional combinatorial optimization problem which aims to maximize the payload without exceeding the capacity of the bag. When one of the problem variables which are ...