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

Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design

In this paper, a multiple objective 'Hybrid Co-evolution based Particle Swarm Optimisation' methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a ε-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method

Optimum SHE for cascaded H-bridge multilevel inverters using: NR-GA-PSO, comparative study

Selective Harmonic Elimination (SHE) is very widely applied technique in the control of multilevel inverters that can be used to eliminate the low order dominant harmonics. This is considered a low frequency technique, in which the switching angles are predetermined based on solving a system of transcendental equations. Iterative techniques such as NR and Heuristic techniques such as GA and PSO have been used widely in literatures for the problem of SHE. This paper presents a detailed comparative study of these three techniques when applied for a 7-level CHB-MLI

Particle Swarm Optimization Framework for Low Power Testing of VLSI Circuits

Power dissipation in sequential circuits is due to increased toggling count of Circuit under Test, which depends upon test vectors applied. If successive test vectors sequences have more toggling nature then it is sure that toggling rate of flip flops is higher. Higher toggling for flip flops results more power dissipation. To overcome this problem, one method is to use GA to have test vectors of high fault coverage in short interval, followed by Hamming distance management on test patterns. This approach is time consuming and needs more efforts. Another method which is purposed in this paper is a PSO based Frame Work to optimize power dissipation. Here target is to set the entire test vector in a frame for time period 'T', so that the frame consists of all those vectors strings which not only provide high fault coverage but also arrange vectors in frame to produce minimum toggling

A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters from the cloud computing environment, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. Traditional data placement strategies maintain load balancing with a given number of datacenters, which results in a large data transmission time. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the impact factors impacting transmission delay, such as the band-width between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover operator and mutation operator of the genetic algorithm were adopted to avoid the premature convergence of the traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing

Kajian terhadap ketahanan hentaman ke atas konkrit berbusa yang diperkuat dengan serat kelapa sawit

Konkrit berbusa merupakan sejenis konkrit ringan yang mempunyai kebolehkerjaan yang baik dan tidak memerlukan pengetaran untuk proses pemadatan. Umum mengenali konkrit berbusa sebagai bahan binaan yang mempunyai sifat kekuatan yang rendah dan lemah terutama apabila bahan binaan ini dikenakan tenaga hentaman yang tinggi. Namun begitu, konkrit berbusa merupakan bahan yang berpotensi untuk dijadikan sebagai bahan binaan yang berkonsepkan futuristik. Binaan futuristik adalah binaan yang bercirikan ringan, ekonomi, mudah dari segi kerja pembinaan dan yang paling penting adalah mesra alam. Dalam kajian ini, konkrit berbusa ditambah serat buangan pokok kelapa sawit untuk untuk meningkatkan sifat kekuatan atau rapuh. Serat kelapa sawit juga berfungsi mempertingkatkan ketahanan hentaman terutamanya aspek nilai penyerapan tenaga hentaman dan nilai tenaga hentaman. Kandungan peratusan serat kelapa sawit yang digunakan adalah 10%, 20% dan 30% dengan dua ketumpatan konkrit berbusa iaitu 1000kg/m3 dan 1400kg/m3 . Untuk menentukan nilai penyerapan tenaga hentaman dan nilai tenaga hentaman, ujikaji Indentasi dan ujikaji hentaman dilakukan ke atas sampel�sampel yang telah diawet selama 28 hari. Luas bawah graf tegasan-terikan yang diperolehi daripada ujikaji Indentasi merupakan nilai penyerapan tenaga hentaman bagi sampel konkrit berbusa. Untuk ujikaji hentaman, keputusan ujikaji dinilai berdasarkan nilai tenaga hentaman untuk meretakkan sampel yang diperolehi daripada mesin ujikaji dynatup. Secara keseluruhannya, hasil dapatan utama bagi kedua-dua ujikaji menunjukkan sampel yang mengandungi peratusan serat kelapa sawit sebanyak 20% mempunyai nilai penyerapan tenaga hentaman dan nilai tenaga hentaman yang tinggi. Serapan tenaga maksimum adalah sebanyak 4.517MJ/m3 untuk ketumpatan 1400kg/m3 . Ini menunjukkan ketumpatan 1400kg/m3 berupaya menyerap tenaga lebih baik berbanding ketumpatan 1000kg/m3 . Manakala untuk nilai tenaga hentaman maksimum adalah sebanyak 27.229J untuk ketumpatan 1400kg/m3 . Hasil dapatan tersebut menunjukkan ketumpatan 1400kg/m3 dengan peratusan serat sebanyak 20% berupaya mengalas tenaga hentaman yang lebih banyak sebelum sampel retak. Kesimpulannya, peningkatan ketumpatan konkrit berbusa dan pertambahan serat buangan kelapa sawit ke dalam konkrit berbusa dapat meningkatkan ciri ketahanan hentaman konkrit berbusa khususnya aspek nilai penyerapan tenaga hentaman dan nilai tenaga hentaman