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different evolutionary algorithms is shown in Fig. 3 . Figure 4 shows the power flow condition of congested lines in all cases before and after congestion. Fig. 3. Comparison of characteristics of

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1 Introduction Evolutionary Algorithms (EAs) are optimization techniques inspired by biological evolution. They solve complex problems across various domains, like engineering, finance, Artificial Intelligence (AI), and data science [ 1 ]. EAs mimic

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evolutionary algorithms and neural network for congestion management in power systems ,” PhD Thesis , Anna University , India , 2011 . [10] S. D. Dao , K. Abhary , and R. Marian , “ An adaptive restarting genetic algorithm for global optimization

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The paper takes an interdisciplinary look at gossip, analyzing it as a living phenomenon, embracing all of its aspects. The root of the word gossip is related, in almost all Indo-European languages, to the idea of weaving and webs or nets. Originally, the word did not seem to have the negative connotations it has now, and which it acquired thanks to the offices of religion, ethic and philosophy. When looked upon with a neutral eye, it will reveal itself as a treasury of human wisdom concerning communication, with insights long preceding those the Internet prompted in social psychology. The paper provides general rules for its dissemination based on the needs that gossip fulfils. These needs can be uncovered by examining the processes of the transformation of gossip.

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Efficient algorithms are needed for optimization of objects and systems, because the user would like to be sure that the optimum is global. The paper shows a very well scalable discrete firefly algorithm, developed for solving a supplier selection problem. The built in general reduced gradient and evolutionary algorithms of the Excel solver are also compared solving this problem. The results show that the firefly algorithm solves the problem in the fragment of the running time of the evolutionary algorithm. In the second part of the article, a mathematical model was formulated to solve the fixed destination multidepot multiple tour multiple traveling salesmen problem (mdmMTSP).

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Computational load balancing plays a prominent role on the success of domain decomposition based finite element computations on parallel computers. Computational load balancing is achieved through mesh partitioning. In this paper, a mesh-partitioning algorithm is proposed for generating sub-meshes with optimal shape using a steady state elite preserving evolutionary algorithm (EA). Numerical experiments have been conducted by solving several practical engineering problems. Comparisons have also been made with its multi-level counter part by solving several benchmark problems. Numerical studies indicate that performance of the proposed mesh-partitioning algorithm is either comparable or superior to the multi-level algorithm.

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Abstract

Nowadays heuristic methods are one of the most used tools for the optimization of problems. The proof of that is the fact that they are widely used in chemistry, economics and energy. Among the most popular of heuristic methods belongs differential evolution, belonging to the so-called ‘evolutionary algorithms’. They can handle difficult, large-scale problems with many parameters, like the optimization of the hydro-thermal coordination of hydro and thermal power plants. As with any other method, differential evolution also has certain parameters. These parameters, among others, are the size of the population, the maximum number of generations, crossover parameter and mutation factor. The effect of these parameters on the results of an optimization using differential evolution is the focus of this paper. The hydro-thermal coordination of one hydro and one thermal power plant was used as an example to explain this issue.

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Horváth, A., Horváth Z. Optimal shape design of diesel intake ports with evolutionary algorithm, Proceedings of 5th European conference on numerical mathematics and advanced applications (ENUMATH 2003) Edited by

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Horváth A., Horváth Z. Optimal shape design of diesel intake ports with evolutionary algorithm, Proceedings of 5th European conference on numerical mathematics and advanced applications (ENUMATH 2003) Edited by

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dynamism provided by the cloud has increased the complexity of timing. Metaheuristic algorithms (especially evolutionary algorithms like the Strength Pareto Evolutionary Algorithm) have been shown to be suitable for optimizing multi

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