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

Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’

Creativity and Autonomy in Swarm Intelligence Systems

This work introduces two swarm intelligence algorithms -- one mimicking the behaviour of one species of ants (\emph{Leptothorax acervorum}) foraging (a `Stochastic Diffusion Search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a `Particle Swarm Optimiser', PSO) -- and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploliting an artistic tension between the local behaviour of the `birds flocking' - as they seek to follow the input sketch - and the global behaviour of the `ants foraging' - as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putative `creativity' of this hybrid swarm system in the philosophical light of the `rhizome' and Deleuze's well known `Orchid and Wasp' metaphor

Creative or Not? Birds and Ants Draw with Muscle

In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined. Next, the novel behaviour of the hybrid algorithm is reflected through a cooperative attempt to make a drawing, followed by a discussion about creativity in general and the ’computational creativity’ of the swarm

Application of Firefly Algorithm and Its Parameter Setting for Job Shop Scheduling

AbstractJob shop scheduling problem (JSSP) is one of the most famous scheduling problems, most of which are categorisedinto Non-deterministic Polynomial (NP) hard problem. The objectives of this paper are to i) present the application of a recent developed metaheuristic called Firefly Algorithm (FA) for solving JSSP; ii) investigate the parameter setting of the proposed algorithm; and iii) compare the FA performance using various parameter settings. The computational experiment was designed and conducted using five benchmarking JSSP datasets from a classical OR-Library. The analysis of the experimental results on the FA performance comparison between with and without using optimised parameter settings was carried out. The FA with appropriate parameters setting that got from the experiment analysis produced the best-so-far schedule better than the FA withoutadopting parameter settings

Full factorial experimental design for parameters selection of Harmony Search Algorithm

AbstractMetaheuristic may be defined as an iterative search process that intelligently performs the exploration and exploitationin the solution space aiming to efficiently find near optimal solutions. Various natural intelligences and inspirations have been artificially embedded into the iterative process. In this work, Harmony Search Algorithm (HSA), which is based on the melody fine tuning conducted by musicians for optimising the synchronisation of the music, was adopted to find optimal solutions of nine benchmarking non-linear continuous mathematical models including two-, three- and four-dimensions. Considering the solution space in a specified region, some models contained a global optimum and multi local optima. A series of computational experiments was used to systematically identify the best parameters of HSA and to compare its performance with other metaheuristics including the Shuffled Frog Leaping (SFL) and the Memetic Algorithm (MA) in terms of the mean and variance ofthe solutions obtained