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Image compression plays a significant role in digital image storage and transmission because of limited availability of storage devices space and insufficient bandwidth and is beneficial for all multimedia applications. Magnetic r...
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Image compression plays a significant role in digital image storage and transmission because of limited availability of storage devices space and insufficient bandwidth and is beneficial for all multimedia applications. Magnetic resonance imaging (MRI) of a human body produces an image of huge size and is to be compressed but medical field demands high image quality for better diagnosis of disease. In this technologically advanced world, intelligence systems try to simulate human intelligence. It is applied in the field of engineering, industry, medicine and education problems and it makes decisions by using the several inputs. However, the search process is enormous and convergence time depends on algorithm structure. In this paper first time methaheuristic algorithms are used for near optimum solutions. This paper introduces flower pollination algorithm (FPA)-based vector quantisation for better image compression with better reconstructed image quality. Performance of proposed method is evaluated by using peak signal to noise ratio (PSNR), mean square error (MSE) and fitness function.
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In design optimisation field, there are many non-linear optimisation problems and the traditional algorithms cannot deal with these problems well. In this paper, we improve the standard particle swarm optimisation (PSO) and propos...
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In design optimisation field, there are many non-linear optimisation problems and the traditional algorithms cannot deal with these problems well. In this paper, we improve the standard particle swarm optimisation (PSO) and propose a new algorithm to solve the overcome of standard PSO algorithm like being trapped easily into a local optimum. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Compared with standard PSO on the benchmark functions, the results show that the new algorithm is efficient. We also used the new algorithm to solve design optimisation problems and the experiment results show the new algorithm is effective for these problems.
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Particle swarm optimisation (PSO) algorithm is easy to fall into local optimum, so an improved PSO based on cellular automata is proposed by combining cellular automata (CA) with PSO. In the proposed CAPSO, each particle of partic...
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Particle swarm optimisation (PSO) algorithm is easy to fall into local optimum, so an improved PSO based on cellular automata is proposed by combining cellular automata (CA) with PSO. In the proposed CAPSO, each particle of particle swarm is considered as cellular automata, and is distributed in two-dimensional grid. The state update of each cell is not only related to its own state and the neighbour state, but also related with the state of the optimal cell. If the state is too close with the optimal cell, then the cell state is re-update. Simulation experiments on typical test functions show that, compared with other algorithms, the proposed algorithm has good robustness, strong local search ability and global optimisation ability, and can solve the optimisation problems effectively.
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Hybrid particle swarm optimisation algorithm is applied to image segmentation problem to determine the threshold in this paper. Based on the analysis of basic particle swarm optimisation algorithm, a hybrid particle swarm optimisa...
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Hybrid particle swarm optimisation algorithm is applied to image segmentation problem to determine the threshold in this paper. Based on the analysis of basic particle swarm optimisation algorithm, a hybrid particle swarm optimisation algorithm which combines the traditional particle swarm algorithm and A"-means cluster algorithm is introduced to segment images, and the detailed computer implementation procedure is given for image segmentation. Numerical experiments have been performed to evaluate the efficiency of hybrid algorithm for optimisation problem of image segmentation.
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A novel self-organising approach to cooperative hunting by robotic swarm is put forward. Each individual can simply detect the direction angle of moving target. By using particle swarm optimisation (PSO), locating target can be re...
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A novel self-organising approach to cooperative hunting by robotic swarm is put forward. Each individual can simply detect the direction angle of moving target. By using particle swarm optimisation (PSO), locating target can be realised through the individual's local interaction. Collective hunting behaviour emerged when human object moved through the detection area. Simulations and experiments demonstrate the feasibility and effectiveness of the proposed approach to cooperative hunting by swarm robotic systems.
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Particle swarm optimisation (PSO) is an intelligent optimisation algorithm with different variants that are developed according to the targeted application. These variants are obtained by changing the parameters of the PSO algorit...
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Particle swarm optimisation (PSO) is an intelligent optimisation algorithm with different variants that are developed according to the targeted application. These variants are obtained by changing the parameters of the PSO algorithm like inertia weight, constriction factor, and acceleration factor. These variants can be utilised for various power system optimisation requirements such as distribution automation, for a targeted application. Distribution network reconfiguration (DNR) is one such application that involves high computational complexity and requires an intelligent optimisation technique for its solution. In this paper different variants of PSO are applied to DNR problem like binary PSO, crazy PSO, and enhanced inter coded PSO for 16 bus and 33 bus test systems. These variants ensure proper exploration and exploitation of the search space to obtain optimum and quick solution for the DNR problem.
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In this paper, modified version of multi-verse optimiser (MVO) was suggested and tested on numerical optimisation problems. MVO is an innovative optimisation approach which stimulated from the concepts of cosmology; they are named...
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In this paper, modified version of multi-verse optimiser (MVO) was suggested and tested on numerical optimisation problems. MVO is an innovative optimisation approach which stimulated from the concepts of cosmology; they are named as white hole, black hole and wormhole. Mathematical modelling of this concept has been carried out to acquire exploitation, exploration and local search. Modification in MVO has been made by introducing concept of dynamic variation in population size (universe). Modified multi-verse optimiser (MMVO) was tested on 16 benchmark functions having different complexity. Statistical comparisons of other algorithms outcomes is depicted that MMVO performs better than other algorithms.
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The non-oriented two-dimensional bin packing problem (NO-2DBPP) deals with a set of integer sized rectangular pieces that are to be packed into identical square bins. The specific problem is to allocate the pieces to a minimum num...
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The non-oriented two-dimensional bin packing problem (NO-2DBPP) deals with a set of integer sized rectangular pieces that are to be packed into identical square bins. The specific problem is to allocate the pieces to a minimum number of bins allowing the pieces to be rotated by 90° but without overlap. In this paper, an evolutionary particle swarm optimisation algorithm (EPSO) is proposed for solving the NO-2DBPP. Computational performance experiments of EPSO, simulating annealing (SA), genetic algorithm (GA) and unified tabu search (UTS) using published benchmark data were studied. Based on the results for packing 3000 rectangles, EPSO outperformed SA and GA. In addition; EPSO results were consistent with the results of UTS indicating that it is a promising algorithm for solving the NO-2DBPP.
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Many power systems in the world today are operating closer to their stability boundaries, and thus it is critical for independent system operators (ISOs) to ensure that systems have adequate stability margins during operation in c...
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Many power systems in the world today are operating closer to their stability boundaries, and thus it is critical for independent system operators (ISOs) to ensure that systems have adequate stability margins during operation in case of unexpected losses of system components. Failure to do so may result in a catastrophic widespread blackout, ie. system voltage collapses. This paper presents a novel memetic algorithm (MA)-based strategy to effectively maximise system voltage stability margins, through the optimum control of automatic voltage regulator (AVR) of generators, on-load tap changer (OLTC) of transformers and the sizes of shunt capacitors (SCs) etc, given any system operating conditions. The proposed strategy can assist ISOs to perform corrective actions to increase stability margins when the system operates too close to the stability boundaries. A mix-integer non-linear programming (MINLP) problem is formulated here using a MA based on the trajectory movement rule of particle swarm optimisation (PSO). By using the MA-based approach, system voltage collapse margins can be improved and these enhancements can then be verified using a continuation power flow (CPF) technique. The feasibility and practicality of this approach has been tested on a 3-machine 9-bus and the IEEE 118-bus power systems.
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Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO,...
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Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.
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