Trajectory optimization for exposure to minimal electromagnetic pollution using genetic algorithms approach: A case study

Low-frequency electromagnetic pollution associated with electricity supplies and electrical appliances creates broad and specific challenges. Among them, knowing the values of this pollution in urban areas to prevent long exposure in the daily life human beings is rising in today's information society. This paper presents a comprehensive approach for, first, mapping electromagnetic pollution of complete urban areas and, second, based on the former data, the trajectories planning of commuting with minimal electromagnetic exposure. In the first stage, the proposed approach reduces the number of necessary measurements for the pollution mapping, estimating their value by optimizing functional criteria using genetic algorithms (GAs) and considering the superposition effect of different sources. In the second stage, a combination of a specifically designed search space and GA as optimization algorithm makes it possible to determine an optimized trajectory that represents a balanced solution between distance and exposure to magnetic fields. The results verify the obtaining of a complete mapping with less error, between 1% and 2.5%, in power lines and medium/low voltage (MV/LV) substations, respectively. The proposed approach obtains optimized trajectories for different types of commuting (pedestrians, bikers, and vehicles), and it can be integrated into mobile applications. Finally, the method was tested on an actual urban area in Malaga (Spain).Financing for open access position: University of Malaga / CBUA

Tracking variable fitness landscape in dynamic multi-objective optimization using adaptive mutation and crossover operators

Abstract: Many real-world problems are modeled as multi-objective optimization problems whose optimal solutions change with time. These problems are commonly termed dynamic multi-objective optimization problems (DMOPs). One challenge associated with solving such problems is the fact that the Pareto front or Pareto set often changes too quickly. This means that the optimal solution set at period t may likely vary from that at (t+1), and this makes the process of optimizing such problems computationally expensive to implement. This paper proposes the use of adaptive mutation and crossover operators for the non-dominated sorting genetic algorithm III (NSGA-III). The aim is to find solutions that can adapt to fitness changes in the objective function space over time. The proposed approach improves the capability of NSGA-III to solve multi-objective optimization problems with solutions that change quickly in both space and time. Results obtained show that this method of population reinitialization can effectively optimize selected benchmark dynamic problems. In addition, we test the capability of the proposed algorithm to select robust solutions over time. We recognize that DMOPs are characterized by rapidly changing optimal solutions. Therefore, we also test the ability of our proposed algorithm to handle these changes. This is achieved by evaluating its performance on selected robust optimization over time (ROOT) and robust Pareto-optimality over time (RPOOT) benchmark problems

Evolutionary Dynamic Multi-Objective Optimisation : A survey

Peer reviewedPostprin

A Novel Dynamic Multiobjective Optimization Algorithm With Hierarchical Response System

Natural Science Foundation of China (Grant Number: 62073271); National Science and Technology Major Project of China (Grant Number: J2019-I-0013-0013); Independent Innovation Foundation of Aero Engine Corporation of China (AECC) of China (Grant Number: ZZCX-2018-017); 10.13039/501100007633-Korea Foundation for Advanced Studies; Science Foundation for Distinguished Young Scholars of the Fujian Province (Grant Number: 2023J06010)