Finite algorithms for linear optimisation problems

Bees and Firefly Algorithms for Noisy Non - Linear Optimisation Problems

— Effective methods for solving the complex and noisy engineering problems using a finite sequence of instructions can be categorised into optimisation and meta-heuristics algorithms. The latter might be defined as an iterative search process that efficiently performs the exploration and exploitation in the solution space aiming to efficiently find near optimal solutions. Various natural intell...

Natural Algorithms for Optimisation Problems

Many computational techniques borrow ideas from nature in one way or another. Neural networks imitate the structure of our human brain, genetic algorithms simulate evolution and swarms of insects inspired algorithms for stochastic combinatorial optimisation. These techniques are characterised by inherent parallelism, adaptivity, positive feedback and some element of randomness. This report deta...

Fuzzy Primal Simplex Algorithms for Solving Fuzzy Linear Programming Problems

Genetic Algorithms for Multiple-Choice Optimisation Problems

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse ...

Algorithms for linear groups of finite rank

Article history: Received 21 January 2013 Available online 16 July 2013 Communicated by Derek Holt