Evolutionary and Adaptive Computing in Engineering Design: The Integration of Adaptive Search Exploration and Optimization with Engineering Design Pro
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An Adaptive Parallel Genetic Algorithm for VLSI-Layout Optimization
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Adaptively choosing niching parameters in a PSO
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Understanding UMTS Radio Network Modelling, Planning and Automated Optimisation: Theory and Practice
Understanding UMTS Radio Network Modelling, Planning and Automated Optimisation: Theory and Practice
Adaptive particle swarm optimization: detection and response to dynamic systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Self-adaptive ant colony optimisation applied to function allocation in vehicle networks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
Barebones particle swarm for multi-objective optimisation problems
International Journal of Innovative Computing and Applications
A new multi-swarm multi-objective particle swarm optimization based on pareto front set
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Cooperative particle swarm optimization for multiobjective transportation planning
Applied Intelligence
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This paper presents MO-TRIBES, an adaptive multiobjective Particle Swarm Optimization (PSO) algorithm. Metaheuristics have the drawback of being very dependent on their parameter values. Then, performances are strongly related to the fitting of parameters. Usually, such tuning is a lengthy, time consuming and delicate process. The aim of this paper is to present and to evaluate MO-TRIBES, which is an adaptive algorithm, designed for multiobjective optimization, allowing to avoid the parameter fitting step. A global description of TRIBES and a comparison with other algorithms are provided. Using an adaptive algorithm means that adaptation rules must be defined. Swarm's structure and strategies of displacement of the particles are modified during the process according to the tribes behaviors. The choice of the final solutions is made using the Pareto dominance criterion. Rules based on crowding distance have been incorporated in order to maintain diversity along the Pareto Front. Preliminary simulations are provided and compared with the best known algorithms. These results show that MO-TRIBES is a promising alternative to tackle multiobjective problems without the constraint of parameter fitting.