1,721,040 research outputs found
Information Sharing Impact of Stochastic Diffusion Search on Population-Based Algorithms
Get PDFThis work introduces a generalised hybridisation strategy which utilises the information sharing mechanism deployed in Stochastic Diffusion Search when applied to a number of population-based algorithms, effectively merging this nature-inspired algorithm with some population-based algorithms. The results reported herein demonstrate that the hybrid algorithm, exploiting information-sharing within the population, improves the optimisation capability of some well-known optimising algorithms, including Particle Swarm Optimisation, Differential Evolution algorithm and Genetic Algorithm. This hybridisation strategy adds the information exchange mechanism of Stochastic Diffusion Search to any population-based algorithm without having to change the implementation of the algorithm used, making the integration process easy to adopt and evaluate. Additionally, in this work, Stochastic Diffusion Search has also been deployed as a global optimisation algorithm, and the optimisation capability of two newly introduced minimised variants of Particle Swarm algorithms is investigated
Swarm Intelligence and Evolutionary Techniques for Real World Applications
Get PDFThis work provides a comprehensive list of real-world applications of swarm intelligence and evolutionary computation technique
Information sharing impact of stochastic diffusion search on population-based algorithms
No full textThis work introduces a generalised hybridisation strategy which utilises the information sharing mechanism deployed in Stochastic Diffusion Search when applied to a number of population-based algorithms, effectively merging this nature-inspired algorithm with some population-based algorithms. The results reported herein demonstrate that the hybrid algorithm, exploiting information-sharing within the population, improves the optimisation capability of some well-known optimising algorithms, including Particle Swarm Optimisation, Differential Evolution algorithm and Genetic Algorithm. This hybridisation strategy adds the information exchange mechanism of Stochastic Diffusion Search to any population-based algorithm without having to change the implementation of the algorithm used, making the integration process easy to adopt and evaluate. Additionally, in this work, Stochastic Diffusion Search has also been deployed as a global optimisation algorithm, and the optimisation capability of two newly introduced minimised variants of Particle Swarm algorithms is investigated.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Tomographic reconstruction with search space expansion
Get PDFA search space expansion process is proposed in the context of tomographic reconstruction (TR). The idea is to widen the effective search space in a series of increasing sizes with clamping on the search space boundary. The technique was tested on four simple phantoms and on the clinically important Shepp-Logan phantom. Dispersive flies optimisation (DFO), a lightweight particle swarm optimisation (PSO) variant, is shown to produce lower reproduction errors compared to standard TR toolbox algorithms. The expansion technique demonstrably decreases salt-and-pepper noise. DFO with 50 subspace searches was found to be superior to differential evolution, PSO and, more importantly, a number of conventional reconstruction techniques. To the best of our knowledge, this is the first work where search space expansion, in its literal form, is introduced, discussed and applied to this problem
Swarm Intelligence and Weak Artificial Creativity
Get PDFSwarm intelligence via its infamous struggle to identify a suitable balance between exploration and exploitation phases, provides a valuable mean to approach artificial creativity. This work deploys two swarm intelligence algorithms, one simulating the behaviour of birds flocking and fish schooling (Particle Swarm Optimisation) and the other mimicking the behaviour of ants foraging (Stochastic Diffusion Search) in order to lay the foundation for a discussion addressing the concepts of freedom and constraint within the topic of creativity in general, and more specifically their impact on the artificial creativity of the underlying systems. An analogy is drawn on mapping these two ‘prerequisites’ of creativity onto the two well-known aforementioned phases of exploration and exploitation in swarm intelligence algorithms. This is accompanied by the visualisation of the behaviour of the swarms whose performance are evaluated in the context of the arguments presented. Additionally in the spirit of Searle’s definition of weak and strong artificial intelligence, a discussion on weak vs. strong artificial creativity in swarm intelligence systems is presented
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