Dalelių spiečių optimizavimo algoritmų taikymo daugiakriteriams uždaviniams efektyvumo tyrimas

In this thesis the problem of evaluating the performance of multi-objective optimization methods for non-convex problems is examined. Namely, the performance of multi-objective particle swarm optimization methods are investigated. An overview of these methods is provided in this thesis covering most methods described in literature. A novel classification system of these methods is developed. This system uses method design choices to classify them. A thorough experimental analysis of existing methods is given. Each method is tested using a wide variety of test problems. The results are further analyzed with regards to what types of problems each method solves best. An important aspect of solution quality when it comes to multi-objective problems is the uniformity of solution spread along the real Pareto frontier. Due to the inadequacies of existing performance indicators when it comes to measuring Pareto frontier approximation solution spread, two new indicators are proposed. These two indicators are designed to capture the intuitive notion of solution spread uniformity. They are discussed in comparison with existing indicators. Two new multi-objective particle swarm optimization methods are proposed in the thesis as well. These methods are based on the idea of heterogeneous swarms - swarms where several different types of particles are used at the same type. The particles share information via the same non-dominated point archive

Evaluating the Performance of Multi-Objective Particle Swarm Optimization Algorithms

In this thesis the problem of evaluating the performance of multi-objective optimization methods for non-convex problems is examined. Namely, the performance of multi-objective particle swarm optimization methods are investigated. An overview of these methods is provided in this thesis covering most methods described in literature. A novel classification system of these methods is developed. This system uses method design choices to classify them. A thorough experimental analysis of existing methods is given. Each method is tested using a wide variety of test problems. The results are further analyzed with regards to what types of problems each method solves best. An important aspect of solution quality when it comes to multi-objective problems is the uniformity of solution spread along the real Pareto frontier. Due to the inadequacies of existing performance indicators when it comes to measuring Pareto frontier approximation solution spread, two new indicators are proposed. These two indicators are designed to capture the intuitive notion of solution spread uniformity. They are discussed in comparison with existing indicators. Two new multi-objective particle swarm optimization methods are proposed in the thesis as well. These methods are based on the idea of heterogeneous swarms - swarms where several different types of particles are used at the same type. The particles share information via the same non-dominated point archive

On the multi-objective optimization aided drawing of connectors for graphs related to business process management

A problem of drawing aesthetically looking graphs is considered. We focus on graphs related to management of business processes. Vertices of a graph are visualized as rectangles (flow objects), and edges are visualized as rectangular connectors (sequence flow). A particular problem of aesthetic drawing is considered where location of vertices is fixed, and the lines representing the edges should be drawn. The latter problem is restated as a graphs oriented multi-objective combinatorial optimization problem. The generally recognized criteria of aesthetic presentation, such as the general length of lines, the number of crossings, and the number of bends, are considered as the objectives to be minimized. The attitude of the potential users of the supposed algorithms towards the relative importance of the considered criteria is elicited by a psychological experiment. The elicited information is used in the development of domain-specific multi-objective optimization algorithms. We propose for that problem a version of the metaheuristics of ant colony optimization. The efficiency is evaluated experimentally using randomized test problems of different complexityTaikomosios informatikos katedraVilniaus universitetasVytauto Didžiojo universiteta

tardis-sn/tardis: TARDIS v2023.10.20

<p>This release has been created automatically by the TARDIS continuous delivery pipeline.</p> <p>A complete list of changes for this release is available at <a href="https://github.com/tardis-sn/tardis/blob/master/CHANGELOG.md">CHANGELOG.md</a>.</p&gt

tardis-sn/tardis: TARDIS v2023.11.05

<p>This release has been created automatically by the TARDIS continuous delivery pipeline.</p> <p>A complete list of changes for this release is available at <a href="https://github.com/tardis-sn/tardis/blob/master/CHANGELOG.md">CHANGELOG.md</a>.</p&gt