evolutionary computation
The fitness landscape of optimization problems significantly impacts the performance of metaheuristic optimization algorithms. While no algorithm performs well on all problems, identifying the most suitable one for a specific problem can be achieved by extracting features from the landscape. However, these features are often extracted before optimization, disregarding valuable knowledge collected during the optimization process. Moreover, existing algorithm selection methods rely on a single algorithm, limiting flexibility and missing potentially better options.
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The uploaded file is the code of the algorithm FCNEA written in MATLAB.
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Decision variables of the best run for each algorithm
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Route planning also known as pathfinding is one of the key elements in logistics, mobile robotics and other applications, where engineers face many conflicting objectives. However, most of the current route planning algorithms consider only up to three objectives. In this paper, we propose a scalable many-objective benchmark problem covering most of the important features for routing applications based on real-world data. We define five objective functions representing distance, traveling time, delays caused by accidents, and two route specific features such as curvature and elevation.
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