| Title |
Evaluating the Performance of Multi-Objective Particle Swarm Optimization Algorithms / |
| Translation of Title |
Dalelių spiečių optimizavimo algoritmų taikymo daugiakriteriams uždaviniams efektyvumo tyrimas. |
| Authors |
Jančauskas, Vytautas |
| Full Text |
|
| Pages |
211 |
| Keywords [eng] |
multi-objective optimization ; particle swarm optimization ; optimization metaheuristics |
| Abstract [eng] |
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. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Doctoral thesis |
| Language |
English |
| Publication date |
2016 |
