Recently, a combination between simulation and optimisation has been important for many of HVAC design problems. Long execution time is usually needed for one simulation run; consequently huge time will be required for the optimisation process. The aim of this study is to evaluate how combinations between optimisation algorithms can achieve faster and/or better solutions for multiobjective opti...

In this paper a constrained nonlinear model predictive control (CNMPC) based on deterministic global optimisation is designed. The approach adopted consists in the transformation of the dynamic optimisation problem into a nonlinear programming (NLP) problem using the method of orthogonal collocation on finite elements. Rigorous convex underestimators of the nonconvex NLP problem are then derive...

The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper we focus on the standard linear nonparametric ICA problem from an optimisation point of view. It is well known that after a pre-whitening process, the problem can be solved via an optimisation approach on a suitable ma...

In electric power systems, finding optimal location and setting of flexible AC transmission system (FACTS) devices represents a difficult optimisation problem. This is due to its discrete, multi-objective, multimodal and constrained nature. Finding near-global solutions in such a problem is very demanding. Brainstorm optimisation algorithm (BSOA) is a novel promising heuristic optimisation algo...

Multi-objective optimisation is regarded as one of the most promising ways for dealing with constrained optimisation problems in evolutionary optimisation. This paper presents a theoretical investigation of a multi-objective optimisation evolutionary algorithm for solving the 0-1 knapsack problem. Two initialisation methods are considered in the algorithm: local search initialisation and greedy...

In this paper, we present the famous temporal partitioning algorithms that temporally partition a data flow graph on reconfigurable systems. We have classified these algorithms into four classes: 1) whole latency optimisation algorithms; 2) whole communication cost optimisation algorithms; 3) whole area optimisation algorithms; 4) whole latency-communication cost optimisation algorithms. These ...

The public health system is plagued by inefficient use of resources. Frequently, the results are lengthy patient treatment waiting times. While many solutions for patient scheduling in health systems exist, few address the problem of coordination between independent autonomous departments. In this study, we describe the use of a distributed dynamic constraint optimisation algorithm (Support Bas...

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...

All optimisation algorithms have parameters that affect their performance and reliability. Usually the default values of these parameters are problem-dependent. Regardless of this fact it is common practice to use some default values that are provided with the optimisation algorithm. Finding the optimal values of these parameters is a computationally expensive optimisation problem also known as...

The problem of cooperation of model predictive control (MPC) algorithm with nonlinear (local) steady-state optimisation is considered. It is important when dynamics of disturbances is comparable with dynamics of the process, thus when application of a classical multi-layer approach may be not efficient. The case concerns a situation when a reliable steady-state process model is nonlinear, often...