Effective powertrain design for the emerging electric vehicle market is a complex, multidisciplinary problem. As such, engineers may often use formal decomposition-based optimisation strategies to partition the problem into more manageable subproblems and then integrate their solutions to obtain an optimal system design. Sometimes, these strategies yield decision variables that consist of highl...

The paper proposes a design procedure for the creation of a robust and effective hybrid algorithm, tailored to and capable of carrying out a given design optimisation task. In the course of algorithm creation, a small set of simple optimisation methods is chosen, out of which those performing best will constitute the hybrid algorithm. The simplicity of the method allows implementing ad-hoc modi...

The combination of local optimisation heuristics and genetic algorithms has been shown to be an effective approach for finding near-optimum solutions to the Travelling Salesman Problem (TSP). In problem domains where the problem can be represented geometrically, such as networks and chemical structures, the combination of local optimisation operators and phenotype genetic operators has also bee...

Abstract: The problem of cooperation of Model Predictive Control (MPC) algorithms with steady-state economic optimisation is investigated in this paper. It is particularly important when the dynamics of disturbances is comparable with the dynamics of the process, since in such a case the classical hierarchical multilayer structure is likely to be not efficient and give the economic yield smalle...

We present an investigation into the design of an evolutionary mechanism for multiagent protocol constraint optimisation. Starting with a review of common population based mechanisms we discuss the properties of the mechanisms used by these search methods. We derive a novel algorithm for optimisation of vectors of real numbers and empirically validate the efficacy of the design by comparing aga...

Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest. In practice, score optimisation with multiple restarts provides a practical and surprisingly successful solution, yet the conditions under which this may be a well founded strategy are poorly understood. In this paper, we prove that the problem of identifying t...

1. Abstract In the past decades evolutionary algorithms have been successfully applied to various design optimisation problems. It has been shown that the representation of the problem is crucial for a high performance of the method. One important aspect is the trade-off between the demands for a high geometric variability in shape generation and a minimum number of optimisation parameters. In ...

The backtesting and optimisation of trading strategies has emerged as an interesting research and experimental problem in both finance and Information Technology (IT) fields. However, it is a non-trivial task to effectively and efficiently optimise trading strategies, not to mention the optimisation in the real-world situations. This paper discusses the application of evolutionary technologies ...

Design optimisation of electrical machines, in particular permanent magnet synchronous disc motors, is very important, but quite a complicated problem. A reasonably simplified form of the design procedure may be attacked by various approaches accumulated into two main topics; classical optimisation techniques (deterministic methods) or genetic algorithms (stochastic methods). Genetic algorithm ...

Many-objective optimisation poses great challenges to evolutionary algorithms. To start with, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimisation, results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the conflict between proximity and diversity, along with increasing time or space...