{"id":370,"date":"2015-02-17T11:03:10","date_gmt":"2015-02-17T11:03:10","guid":{"rendered":"http:\/\/www.etfos.unios.hr\/ijeces-test\/?p=370"},"modified":"2017-03-23T08:04:47","modified_gmt":"2017-03-23T08:04:47","slug":"a-comparison-of-several-heuristic-algorithms-for-solving-high-dimensional-optimization-problems","status":"publish","type":"post","link":"http:\/\/www.etfos.unios.hr\/ijeces\/vol-5-no-1-2014\/a-comparison-of-several-heuristic-algorithms-for-solving-high-dimensional-optimization-problems\/","title":{"rendered":"A Comparison of Several Heuristic Algorithms for Solving High Dimensional Optimization Problems."},"content":{"rendered":"
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Emmanuel Karlo Nyarko, Robert Cupec, Damir Filko<\/h3>
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Abstract<\/strong><\/div>\n
The number of heuristic optimization algorithms has exploded over the last decade with new methods being proposed constantly. A recent overview of existing heuristic methods has listed over 130 algorithms. The majority of these optimization algorithms have been designed and applied to solve real-parameter function optimization problems, each claiming to be superior to other methods in terms of performance. However, most of these algorithms have been tested on relatively low dimensional problems, i.e., problems involving less than 30 parameters. With the recent emergence of Big Data, the existing optimization methods need to be tested to find those (un)suitable to handle highly dimensional problems. This paper represents an initial step in such direction. Three traditional heuristic algorithms are systematically analyzed and tested in detail for problems involving up to 100 parameters. Genetic algorithms (GA), particle swarm optimization (PSO) and differential evolution (DE) are compared in terms of accuracy and runtime, using several high dimensional standard benchmark functions.<\/div>\n
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Keywords<\/strong><\/div>\n
heuristic optimization, high dimensional optimization, nature-inspired algorithms, optimization techniques<\/div>\n
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