Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.Comment: 14 page

Optimising continuous microstructures: a comparison of gradient-based and stochastic methods

This work compares the use of a deterministic gradient based search with a stochastic genetic algorithm to optimise the geometry of a space frame structure. The goal is not necessarily to find a global optimum, but instead to derive a confident approximation of fitness to be used in a second optimisation of topology. The results show that although the genetic algorithm searches the space more broadly, and this space has several global optima, gradient descent achieves similar fitnesses with equal confidence. The gradient descent algorithm is advantageous however, as it is deterministic and results in a lower computational cost

A decision support methodology for process in the loop optimisation

Experimental optimisation with hardware-in-the-loop is a common procedure in engineering, particularly in cases where accurate modelling is not possible. A common methodology to support experimental search is to use one of the many gradient descent methods. However, even sophisticated and proven methodologies such as Simulated Annealing (SA) can be significantly challenged in the presence of significant noise. This paper introduces a decision support methodology based upon Response Surfaces (RS), which supplements experimental management based on variable neighbourhood search, and is shown to be highly effective in directing experiments in the presence of significant signal to noise (S-N) ratio and complex combinatorial functions. The methodology is developed on a 3-dimensional surface with multiple local-minima and large basin of attraction, and high S-N ratio. Finally, the method is applied to a real-life automotive experimental application

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits

Swarm optimization for adaptive phase measurements with low visibility

Adaptive feedback normally provides the greatest accuracy for optical phase measurements. New advances in nitrogen vacancy centre technology have enabled magnetometry via individual spin measurements, which are similar to optical phase measurements but with low visibility. The adaptive measurements that previously worked well with high-visibility optical interferometry break down and give poor results for nitrogen vacancy centre measurements. We use advanced search techniques based on swarm optimisation to design better adaptive measurements that can provide improved measurement accuracy with low-visibility interferometry, with applications in nitrogen vacancy centre magnetometry.Comment: 8 pages, 7 figures, comments welcom

Comparison of Methods of Pump Scheduling in Water Supply Systems

In the domestic water supply industry, the reduction of pumping costs is a continuing objective. With the efficient scheduling of pumping operations, it is considered that 10% of the annual expenditure on energy and related costs may be saved. A typical cost function will include all of the expenditure caused by the pumping process and also consider the electrical cost of pumping taking into account the various electrical tariffs, as well as peak demand and pump switching costs. Using only fixed speed pumps, it is possible to use an efficient dynamic programming based method, provided that the storage reservoir levels are known. Other techniques that are showing fruitful results in optimisation are genetic programming and simulated annealing. This paper compares these methods and discusses which is more appropriate in this type of pump scheduling problem

Towards 'smart lasers': self-optimisation of an ultrafast pulse source using a genetic algorithm

Short-pulse fibre lasers are a complex dynamical system possessing a broad space of operating states that can be accessed through control of cavity parameters. Determination of target regimes is a multi-parameter global optimisation problem. Here, we report the implementation of a genetic algorithm to intelligently locate optimum parameters for stable single-pulse mode-locking in a Figure-8 fibre laser, and fully automate the system turn-on procedure. Stable ultrashort pulses are repeatably achieved by employing a compound fitness function that monitors both temporal and spectral output properties of the laser. Our method of encoding photonics expertise into an algorithm and applying machine-learning principles paves the way to self-optimising `smart' optical technologies