Evolutionary Algorithms for Multicriteria Optimization Of Program Module Allocations

In this paper, three evolutionary algorithms have been discussed for solving three-criteria optimization problem of finding a set of Pareto-optimal program module assignments. An adaptive evolutionary algorithm has been recommended for solving an established multiobjective optimization problem. Moreover, a multi-criterion genetic algorithm and an evolution strategy have been considered. Some numerical results have been submitted

Some paradigms of artificial intelligence in financial computer systems

W artykule omówiono wybrane paradygmaty sztucznej inteligencji w kontekście ich zastosowań w informatycznych systemach finansowych. Zaproponowane podejście cechuje się istotnym potencjałem w zakresie wzrostu konkurencyjności przedsiębiorstw, w tym instytucji finansowych. Wymaga jednak efektywnego wykorzystania superkomputerów, gridów i chmur obliczeniowych, W tym kontekście odniesiono się do środowiska obliczeniowego dla cyberwaluty Bitcoin. Ponadto, scharakteryzowano programowanie genetyczne i sztuczne sieci neuronowe do przygotowania strategii inwestycyjnych na giełdzie papierów wartościowych.The article discusses some paradigms of artificial intelligence in the context of their applications in computer financial systems. The proposed approach has a significant potential to increase the competitiveness of enterprises, including financial institutions. However, it requires the effective use of supercomputers, grids and cloud computing. A reference is made to the computing environment for Bitcoin. In addition, we characterized genetic programming and artificial neural networks to prepare investment strategies on the stock exchange market

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics

Pareto-optimal Load Solutions in the I-Banking by Evolutionary Algorithm

Load balancing of the Web bank servers can be implemented by minimization of the workload of the bottleneck Web server. Load balancing improves both a performance of the system and the safety of the bottleneck computers. An evolutionary algorithm based on a tabu search procedure is discussed for solving multi-criteria optimization problem of finding a set of Pareto-suboptimal task assignments. A tabu mutation is used for minimization the workload of the bottleneck computer

Harmony Search for Data Mining with Big Data

Part 8: Intelligent Distributed SystemsInternational audienceIn this paper, some harmony search algorithms have been proposed for data mining with big data. Three areas of big data processing have been studied to apply new metaheuristics. The first problem is related to MapReduce architecture that can be supported by a team of harmony search agents in grid infrastructure. The second dilemma involves development of harmony search in preprocessing of data series before data mining. Moreover, harmony search as a classification algorithm is studied as the third application. Finally, some outcomes for numerical experiments are submitted

Harmony Search for Self-configuration of Fault–Tolerant and Intelligent Grids

Part 8: Intelligent Distributed SystemsInternational audienceIn this paper, harmony search algorithms have been proposed to self-configuration of intelligent grids for big data processing. Self-configuration of computer grids lies in the fact that new computer nodes are automatically configured by software agents and then integrated into the grid. A base node works due to several configuration parameters that define some aspects of data communications and energy power consumption. We propose some optimization agents that are based on harmony search to find a suboptimal configuration of fault–tolerant grids processing big data. Criteria such as probability that all tasks meet their deadlines and also a reliability of grid are considered. Finally, some experimental results have been considered