Evolutionary algorithms have been extensively explored and applied in optimization problems. They allow work with multiple solutions simultaneously, multimodal functions dynamic problems, do not require additional information. Several developed over the years for this task. Yet special attention is needed area of increasing convergence speed evolutionary algorithms. This study aimed at developing a framework capable addressing new line research field computation. We used Gaussian Mixture...

The probabilistic behavior study of Evolutionary Algorithms (EA) in every generation is relevant to perform exploratory analysis, order summarize, monitor, and formulate a hypothesis about observed data. For the purpose understanding better how population evolves along generations, we made descriptive analysis Differential Evolution (DE) evolving population. objective was find model fit over generation. This can be known probability distribution or latent variable model, i.e., Gaussian...

When solving a multi-objective optimization problem using Evolutionary Algorithms, the diversity loss can occur as evolution process is made. This particularly significant in Pareto-based strategies where mechanism required to maintain set of solutions well distributed Pareto Front extension. Therefore, algorithms are with ability keep good balance between exploration and exploitation. To address this challenge, new algorithm proposed considering past generations establish trends population...

A Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) usually detects a change in an environment and responds to its dynamics, which can lead new optimal solutions over time. However, some real problems, correct detection cannot be guaranteed. The existing methods miss changes when there is noise the landscape, or they yield false positives, demanding algorithm respond nonexistent scenario. To handle DMOPs without such detection, DMOEA was proposed diversity inserted into population by...

Traditional Combinatorial Reverse Auctions (CRAs) (multiple items and single or multiple attributes) have been effectively adopted in several real-world applications. However, looking for the best solution (a set of winning sellers) is difficult to solve due CRAs complexity. The use exact algorithms quite unsuitable some real-life auctions exponential time cost these algorithms. Hence, we opted multi-objective evolutionary optimization methods (MOEAs) find compromising solutions. This paper...

Trajectory planning is a crucial issue for robotics. In recent years, researchers have used meta-heuristics, such as Multi-Objective Evolutionary Algorithms (MOEAs), to handle it. However, despite the numerous favorable features of EAs, research needed analyze efficiency and effectiveness algorithms find an optimal trajectory. For this reason, we present comparative study between different Pareto-based MOEAs trajectory mobile manipulator in environment with obstacles. order generate joint...

Evolutionary algorithms have been widely explored and applied in optimization problems. The introduction of multi-objective evolutionary (MOEAs) has facilitated the adaptation creation new methods to handle more complex realistic optimizations, such as dynamic problems (DMOPs). A MOEA (DMOEA) can be constructed by changing structure variation operators used solve DMOPs. Furthermore, DMOEAs implement change-detection strategies mechanisms dynamics environment. are often designed unconstrained...

This paper considers the use of Model-Free Adaptive Control (MFAC) for Continuously Stirred Tank Reactor, a nonlinear system working with and without disturbances.Finding optimal set MFAC parameters is still complex open problem that may be subject to multiple conflicting requirements.However, only small number approaches proposed in literature consider this adjustment task as multi-objective problem.Using Multi-objective Evolutionary Algorithm (MOEA) seems an appropriate approach adjust...