Proceedings Article10.1109/CEC.2002.1004494
Multiobjective optimization using dynamic neighborhood particle swarm optimization
Xiaohui Hu,R.C. Eberhart +1 more
- 12 May 2002
- Vol. 2, pp 1677-1681
694
TL;DR: This paper presents a particle swarm optimization algorithm modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives for multiobjective optimization problems.
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Abstract: This paper presents a particle swarm optimization (PSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Several benchmark cases were tested and showed that PSO could efficiently find multiple Pareto optimal solutions.
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Citations
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References
Particle swarm optimization
James Kennedy,Russell C. Eberhart +1 more
- 06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
44.1K
A new optimizer using particle swarm theory
Russell C. Eberhart,James Kennedy +1 more
- 04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
16.4K
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
- 04 May 1998
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
11K
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
•Book
Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications
Eckart Zitzler
- 27 Dec 1999
TL;DR: The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.
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