As a global search approach based on population evolution, the genetic algorithm (GA) has great advantage in solving MOPs. For most of the multi-objective genetic algorithms (MOGAs), a large size of evolutionary population is adopted in the process of fitness evaluation and selection operation to...
In section “Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl)”, we delve into the implementation of the proposed Iterative map-based Self-Adaptive Crystal Structure Algorithm (SaCryStAl) to address the multi-objective energy management problem. Section “Modeling of a microgri...
Micro Aerial Vehicle Path Planning and Flight with a Multi-objective Genetic AlgorithmDue to its importance for robotics applications, robotic path planning has been extensively studied. Because optimal solutions can be computationally expensive, the need for good approximate......
Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs),
MULTI-OBJECTIVE GENETIC-FUZZY OPTIMAL DESIGN OF PI CONTROLLER IN THE INDIRECT FIELD ORIENTED CONTROL OF AN INDUCTION MOTOR n this paper, a novel multi-objective optimization method based on Genetic-Fuzzy Algorithm (GFA) is proposed. GFA is applied to optimize the five PI contro... M Moalem,B...
3.1. Multi-Objective Algorithms The Multiple Objective Particle Swarm Optimization (MOPSO) algorithm is commonly used to solve optimization problems. It has the following characteristics: does not depend on the problem information and uses real numbers for the solution; the algorithm is highly general;...
A fast and elitist multiobjective genetic algorithm: NSGA-II IEEE Trans Evol Comput, 6 (2) (2002), pp. 182-197, 10.1109/4235.996017 View at publisherView in ScopusGoogle Scholar [51] Biscani F., Izzo D. A parallel global multiobjective framework for optimization: pagmo J Open Source Softw...
‘geneticalgorithm’83. The library’s source code was modified to tailor the algorithm towards the microcomb generation task in this work. In particular, the crossover and parent selection operations were customized. The fitness function and multi-objective formulation of the optimization problem ...
The micro multi-objective genetic algorithm with high efficiency was adopted. 提出一种高效的薄板冲压成形变压边力多目标优化方法,该方法以减少冲压件的成形缺陷为优化目标,以变压边力曲线的特征参数为优化变量,采用自主开发的微型多目标遗传算法作为优化算法,并在优化过程中引入神经网络近似模型以减少数值模拟的次...
The optimization analysis employs different design of Experiment (DOE) techniques to make initial population that is governed by multi-objective genetic algorithm. Hence, the robust design is achieved for 3-axis micro machine tool using the essential knowledge base. The technique is used to remove ...