The overall complexity of optimisation problems in ship design results in high computation time requirements. The high computation time requirements for objective function evaluations in a complex design optimisation problem can practically be reduced by either using parallel execution of the objective functions in state-of-the-art super computers or concurrent execution of the objective functi...

A new version of Genetic Algorithms, the Breeder Genetic Algorithms, has been recently proposed in literature and successfully applied to the continuous parameter optimisation. In this paper we aim to test this technique against a classical discrete Genetic Algorithm on a typical optimisation problem in Aerodynamics, the problem of determining the coordinates of an airfoil given a surface press...

We present a map from the travelling salesman problem (TSP), prototypical NP-complete combinatorial optimisation task, to ground state associated with system of many-qudits. Conventionally, TSP is cast into quadratic unconstrained binary (QUBO) problem, that can be solved on an Ising machine. The size corresponding physical system's Hilbert space $2^{N^2}$, where $N$ number cities considered in...

We examine a multidimensional optimisation problem in the tropical mathematics setting. The problem involves the minimisation of a nonlinear function defined on a finite-dimensional semimodule over an idempotent semifield subject to linear inequality constraints. We start with an overview of known tropical optimisation problems with linear and nonlinear objective functions. A short introduction...

The problem of learning Bayesian network structure is well known to be NP–hard. It is therefore very important to develop efficient approximation techniques. We introduce an algorithm that within the framework of influence diagrams translates the structure learning problem into the strategy optimisation problem, for which we apply the Chen’s self–annealing stochastic optimisation algorithm. The...

In many optimisation problems, analysts are often confronted with multiobjective decision problems. The most common purpose of an analysis is to choose the best trade-offs among all the defined and conflicting objectives. However, many optimisation studies are formulated as a problem whose goal is to find the “best” solution, which corresponds to the minimum or maximum value of a single objecti...

The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions within polynomial time. On the other hand, genetic algorithms are well suited for solving the knapsack problem and they find reasonably good solutions quickly. A naturally arising question is whether genetic algorithms ar...

When attempting to solve a multi-variate optimisation problem, it is often a wise strategy to sacrifice a locally-optimal solution in the hope of finding a better global solution. Algorithms that solve optimisation problems in this manner are sometimes called hill climbing algorithms . Recently, several new hillclimbing approaches have been proposed and have gained in popularity for solving spe...