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

A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

Particle Swarm Optimization (PSO) and two real world applications

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Gerardo Gómez Muntané[en] Particle Swarm Optimization (PSO) belongs to a powerful family of optimization techniques inspired by the collective behaviour of social animals. This method has shown promising results in a wide range of applications, especially in computer science. Despite this, a great popularity of such method has not been achieved. Since we believe in the potential of PSO, we propose the following scheme to be able to take advantage of its properties. First, an implementation from scratch in C language of the method has been done, as well as an analysis of its parameters and its performance in function minimization. Then, a second more specific part of this thesis is devoted to the adaptation of the method for solving two real-world applications. The first one, in the field of signal analysis, consists of an optimization method for the numerical analysis of Fourier functions, whereas the second, in the field of computer science, comprises the optimization of neural networks weights’ for some small architectures

Particle Swarm Optimization (PSO) and two real world applications

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Gerardo Gómez Muntané[en] Particle Swarm Optimization (PSO) belongs to a powerful family of optimization techniques inspired by the collective behavior of social animals. This method has shown promising results in a wide range of applications, especially in computer science. Despite this, a great popularity of such method has not been achieved. Since we believe in the potential of PSO, we propose the following scheme to be able to take advantage of its properties. First, an implementation from scratch in C language of the method has been done, as well as an analysis of its parameters and its performance in function minimization. Then, a second more specific part of this thesis is devoted to the adaptation of the method for solving two real-world applications. The first one, in the field of signal analysis, consists of an optimization method for the numerical analysis of Fourier functions, whereas the second, in the field of computer science, comprises the optimization of neural networks weights’ for some small architectures

Recent tendencies in the use of optimization techniques in geotechnics:a review

The use of optimization methods in geotechnics dates back to the 1950s. They were used in slope stability analysis (Bishop) and evolved to a wide range of applications in ground engineering. We present here a non-exhaustive review of recent publications that relate to the use of different optimization techniques in geotechnical engineering. Metaheuristic methods are present in almost all the problems in geotechnics that deal with optimization. In a number of cases, they are used as single techniques, in others in combination with other approaches, and in a number of situations as hybrids. Different results are discussed showing the advantages and issues of the techniques used. Computational time is one of the issues, as well as the assumptions those methods are based on. The article can be read as an update regarding the recent tendencies in the use of optimization techniques in geotechnics

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing