• A Multiple Objective Particle Swarm Optimization approach using Crowding Distance and Roulette Wheel (Robson Alcântara)
  • A new approach based in Particle Swarm Optimization (PSO) for multiple objective optimization problems. PSO is modified by using the mechanisms crowding distance and roulette wheel, specifically on particles social leader selection and in the deletion method of solutions of the external archive. Another proposed change is the mechanism to update the particles cognitive leader. The performance of this approach is evaluated from test functions and metrics of the traditional literature. The results show that the proposed approach is highly competitive with traditional approaches.


  • A Performance Comparison of Multi-objective Optimization Evolutionary Algorithms for All-Optical Networks Design (Danilo Araújo and Erick Barboza)
  • In this research we investigate the performance of well known multi-objective optimization evolutionary algorithms (MOEA) applied to the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to both minimize the total cost to build the network, i.e. the capital expenditure, and to maximize the overall network performance. We used the network blocking probability to assess the quality of the network service. We have considered the following five different MOEAs: NSGAII, SPEA2, PESAII, PAES and MODE. In order to suggest a suitable algorithm to solve the problem, we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions. We used four well known metrics to compare the achieved Pareto Fronts: hypervolume, spacing, maximum spread and coverage. From our results, we believe that the NSGAII and the SPEA2 algorithms are more suitable to solve this specific problem.


  • MOPSO-CDR with Speciation (Péricles Barbosa)
  • In this work, a new MOPSO technique is proposed to improve the solutions’ convergence, through the creation of an External Archive Manager and a Decision Executer. The search for best solutions avoiding stagnation is one of the main purposes of this technique, aimed to improve the exploration convergence capability of the swarm and provide high quality solutions. Comparisons with well known techniques were performed, where the simulations’ results have shown that our proposal out performed in many cases well known techniques such as MOPSO-CDR and MOPSO. At last, we performed, an analysis in the beginning of the search process and we checked a superior convergence capacity of our proposal.