The Modify Version of Artificial Bee Colony Algorithm to solve Real Optimization problems

The Artificial Bee Colony(ABC) algorithm is one of the best applicableoptimization algorithm. In this work, we make some modifications toimprove the ABC algorithm based on convergence speed of solution. Inorder to, we add some conditions to selected food sources by bees. So, ifsolution have been enough near to optimal solution, then further search existaround the food sources. That, this is near to optimal solution because, wecan replace lower and upper bounds of food sources with smaller valuesrelate to last search. Therefore, the new search is near to optimal solution and after some iteration, optimal solution achieves. Finally, we illustrateconvergence speed of the MABC algorithm that is faster than ABCalgorithm. There are some examples.DOI:http://dx.doi.org/10.11591/ijece.v2i4.42

Optimal fuzzy iterative learning control based on artificial bee colony for vibration control of piezoelectric smart structures

Combining P-type iterative learning (IL) control, fuzzy logic control and artificial bee colony (ABC) algorithm, a new optimal fuzzy IL controller is designed for active vibration control of piezoelectric smart structures. In order to accelerate the learning speed of feedback gain, the fuzzy logic controller is integrated into the ANSYS finite element (FE) models by using APDL (ANSYS Parameter Design Language) approach to adjust adaptively the learning gain of P-type IL control. For improving the performance and robustness of the fuzzy logic controller as well as diminishing human intervention in the operation process, ABC algorithm is used to automatically identify the optimal configurations for values in fuzzy query table, fuzzification parameters and defuzzification parameters, and the main program of ABC algorithm is operated in MATLAB. The active vibration equations are driven from the FE equations for the dynamic response of a linear elastic piezoelectric smart structure. Considering the vibrations generated by various external disturbances, the optimal fuzzy IL controller is numerically investigated for a clamped piezoelectric smart plate. Results demonstrate that the proposed control approach makes the feedback gain has a fast learning speed and performs excellent in vibration suppression. This is demonstrated in the results by comparing the new control approach with the P-type IL control

Nature-Inspired Algorithm for Solving NP-Complete Problems

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015.High-Performance Computing has become an essential tool in numerous natural sciences. The modern highperformance computing systems are composed of hundreds of thousands of computational nodes, as well as deep memory hierarchies and complex interconnect topologies. Existing high performance algorithms and tools already require courageous programming and optimization efforts to achieve high efficiency on current supercomputers. On the other hand, these efforts are platform-specific and non-portable. A core challenge while solving NP-complete problems is the need to process these data with highly effective algorithms and tools where the computational costs grow exponentially. This paper investigates the efficiency of Nature-Inspired optimization algorithm for solving NP-complete problems, based on Artificial Bee Colony (ABC) metaheuristic. Parallel version of the algorithm have been proposed based on the flat parallel programming model with message passing for communication between the computational nodes in the platform and parallel programming model with multithreading for communication between the cores inside the computational node. Parallel communications profiling is made and parallel performance parameters are evaluated on the basis of experimental results.The results reported in this paper are part of the research project, Center of excellence "Supercomputing Applications" - DCVP 02/1, supported by the National Science Fund, Bulgarian Ministry of Education and Science

Genetic learning particle swarm optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO