Julio E. Alvarez-Benitez

Artificial Intelligence · 2005

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 7 citations into it; #28,700 of 35,345 by trust here, #31,361 by citations
• Computational Theory and Mathematics — 6 citations into it; #9,838 of 12,182 by trust here, #9,804 by citations

Whom Julio E. Alvarez-Benitez trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Carlos Artemio Coello Coello
2. Frans van den Bergh
3. Xiaohui Hu
4. Konstantinos E. Parsopoulos
5. Sanaz Mostaghim
6. Jonathan Edward Fieldsend
7. Kalyanmoy Deb
8. Maximino Salazar Lechuga
9. R. Eberhart
10. Michael N. Vrahatis

Who trusts Julio E. Alvarez-Benitez overall

1. Margarita Reyes-Sierra
2. Carlos Artemio Coello Coello

Papers from Julio E. Alvarez-Benitez's perspective

Ordered by how strongly the researchers Julio E. Alvarez-Benitez trusts endorse each paper. Same corpus, a different order for every researcher.

1. MOPSO: a proposal for multiple objective particle swarm optimization (Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600))
2. Multiobjective optimization using dynamic neighborhood particle swarm optimization (Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600))
3. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) (Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706))
4. Handling multiple objectives with particle swarm optimization (IEEE Transactions on Evolutionary Computation)
5. Scalable multi-objective optimization test problems (Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600))
6. An analysis of particle swarm optimizers
7. Particle swarm optimization method in multiobjective problems (ACM Symposium on Applied Computing)
8. A Multi-Objective Algorithm based upon Particle Swarm Optimisation, an Efficient Data Structure and
9. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (Evolutionary Computation)
10. Genetic Programming: On the Programming of Computers by Means of Natural Selection

Their papers

• A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts (2005) — 325 citations