Bottom Line: Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark …

In this paper, we design a parallel genetic algorithm that divides the coverage problem of IoTs with massive nodes into many small problems and then solves these problems using Hadoop in...

In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by …

Aug 13, 2023 · This chapter first reviews multi-objective evolutionary and genetic algorithms and then presents the fundamental principles and design considerations of MOGAs such as …

Multiobjective Genetic Algorithms The primary questions when developing genetic algorithms for multiobjective problems are: how to evaluate fitness, how to incorporate the idea of Pareto …

Multi-Objective Optimization Problems (MOOP) Involve more than one objective function that are to be minimized or maximized Answer is set of solutions that define the best tradeoff between …

Figure 1 shows the flowchart of Multi-objective Genetic Algorithm. In this study chromosomes are coded by real values. To handle the constraints in MOGA, the natural self-adaptation...

Multi - Objective Genetic Algorithm uses a crowding distance metric to create a homogeneous distribution of the non-dominated points on the Pareto front. The convergence criteria is satisfied.

Jun 26, 2019 · Multi-objectives Genetic Algorithm (MOGA) is one of many engineering optimization techniques, a guided random search method. It is suitable for solving multi …

Flow chart of the multi-objective genetic algorithm (MOGA) optimization process. [...] This work focuses on developing a novel method to optimize the fabrication conditions of polyamide...