Elastic Optical Networks (EONs) have emerged as a promising solution to meet the increasing demands for bandwidth and flexibility in modern communication networks. However, efficient resource allocation, including Routing, Spectrum, and Core Assignment (RSCA), represents a critical challenge due to its computational complexity. Given the NP-hard nature of RSCA, this work relies on heuristic and metaheuristic optimization techniques, inspired by Genetic Algorithms (GA). Considering a multicore optical fibers EON and a set of unicast requests, these algorithms seek to compute the path, the index core in each optical link, and frequency slots for each request that minimizes the blocking rate, average of the spectrum used rate, and standard deviation of the spectrum used rate, subject to spectrum continuity, spectrum contiguity, and spectrum non-overlapping assignment. This study applies the proposed algorithm under incremental semi-dynamic traffic and evaluates the performance of core selection policies of first fit (FF), minimum cost given as the number of slots in neighboring cores (MC), minimum cost weighted between number of slots in neighboring cores and the local core (MW). The results show the suitability of the MC and MW approaches over the traditional FF approach.

Software-defined networking (SDN) and network functions virtualization (NFV) are promising technologies for demand services that require building flexible multicast transmission mechanisms with requirements for data processing functions at the network nodes. The multicast routing problem in NFVSDN networks seeks to compute multicast-routing trees and place virtual network functions (VNFs), satisfying the traffic demand with optimal resource use and fair data transmission. Since the problem is computationally complex with conflicting objective functions, this paper approaches multicast routing and VNF placement as a multiobjective optimization problem (MOP), minimizing the total resource cost and the maximum transmission delay variance. In this context, this study develops solutions based on Multiobjective Evolutionary Algorithms (MOEAs). Simulations performed on test instances show that the proposals are promissory by computing efficient and non-dominated solutions when compared to a state-of-the-art mono-objective approach.

Searching for adequate input parameters of a Contrast Enhancement Algorithm is a fundamental task, aimed to get more suitable images, in terms of contrast, for decision making or further applications. Several Optimization approaches take a single image as the input of the process, so they get poor performance. In this proposal, a group of several images of the same type are taken as input of a Robust Multi-Objective Particle Swarm Optimization approach, in order to achieve more suitable input parameters for a kind of images, instead of a single image. The results are a set of input parameters for the Contrast Enhancement Algorithm, suitable for Contrast Enhancement of a group of images of the same type.

In certain medical images, it is possible to achieve contrast enhancement at different levels, in order to highlight different structures present therein. This could be useful to medical specialists to perform more specific diagnoses, in chest radiographs and mammograms, where it is possible to highlight different details when contrast is enhanced. Parameter tuning for Contrast Limited Adaptive Histogram Equalization (CLAHE) using a multi-objective meta-heuristic (SMPSO) is proposed, where the objective functions are the maximization of the amount of information available (via Entropy) and minimization of distortion in the resulting images (Structural Similarity Index, SSIM) simultaneously. The results show that our approach calculates a set of non-dominated solutions or Pareto Set, which represents images with different contrast levels and different levels of commitment between Entropy and Structural Similarity Index. Particularly, these objective functions are contradictory. These enhanced images provide useful information for decision making of specialists.