A Multi-Objective Genetic Algorithm Based on Fitting and Interpolation

Han, Chuang; Wang, Ling; Zhang, Zhaolin; Xie, Jian; Xing, Zijian

doi:10.1109/access.2018.2829262

Cited by 26 publications

(15 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the multi-objective genetic algorithm (NSGA) [24][25] is used to solve the model. A series of non-inferior solutions can be obtained by using NSGA and the Pareto Front is composed of a series of non-inferior solutions.…”

Section: Solution Methodsmentioning

confidence: 99%

Integrated Optimal Dispatching Strategy Considering Power Generation and Consumption Interaction

Liu

Xiao

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Section: Solution Methodsmentioning

confidence: 99%

Integrated Optimal Dispatching Strategy Considering Power Generation and Consumption Interaction

Liu

Xiao

et al. 2021

IEEE Access

View full text Add to dashboard Cite

“…Genetic algorithms are optimization search algorithms that maximize or minimize given objective functions. GA-based methods are flexible methods that can be used to solve various types of problems which can be formulated as an optimization task [31].…”

Section: A the Formulation Of Ga-based Btsp Methodsmentioning

confidence: 99%

A New Evolutionary Multiobjective Model for Traveling Salesman Problem

Chen

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The traveling salesman problem (TSP) is one of the most classical NP-hard problems in the combinatorial optimization, as many practical problems, such as scheduling problems and vehicle-routing cost allocation problems can be abstracted. The introduction of multiobjective in the TSP is a very important research topic, which brings serious challenges to the TSP. Currently, genetic algorithms (GAs) are one of the most effective methods to solve the multiobjective traveling salesman problem (MOTSP). However, GA-based algorithms suffer the premature convergence, the insufficient diversity, and nonuniform distribution of solutions when solving the MOTSP, which further restrict the wide application of GA-based algorithms. In order to overcome these problems, this paper proposes an improved method for GAs based on a novel evolutionary computational model, named the Physarum-inspired computational model (PCM). Based on the prior knowledge of the PCM, the initialization of the population in the proposed method is first optimized to enhance the distribution of solutions. Then, the hill climbing (HC) method is used to improve the diversity of individuals and avert running into the local optimum. Compared to the other MOTSP solving algorithms, a series of experimental results demonstrate that our proposed method achieves a better performance. INDEX TERMS Bi-objective traveling salesman problem, NSGA-II, hill climbing, Physarum.

show abstract

“…Heuristic information is used for navigating the search space for potential individuals, and this can achieve globally optimal solutions. Since then, there have been many works that used GAs in practice [7][8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%

Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests

Bui

Nguyen

Huynh

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

Education is mandatory, and much research has been invested in this sector. An important aspect of education is how to evaluate the learners’ progress. Multiple-choice tests are widely used for this purpose. The tests for learners in the same exam should come in equal difficulties for fair judgment. Thus, this requirement leads to the problem of generating tests with equal difficulties, which is also known as the specific case of generating tests with a single objective. However, in practice, multiple requirements (objectives) are enforced while making tests. For example, teachers may require the generated tests to have the same difficulty and the same test duration. In this paper, we propose the use of Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) for generating k tests with multiple objectives in a single run. Additionally, we also incorporate Simulated Annealing (SA) to improve the diversity of tests and the accuracy of solutions. The experimental results with various criteria show that our approaches are effective and efficient for the problem of generating multiple tests.

show abstract

A Multi-Objective Genetic Algorithm Based on Fitting and Interpolation

Cited by 26 publications

References 40 publications

Integrated Optimal Dispatching Strategy Considering Power Generation and Consumption Interaction

Integrated Optimal Dispatching Strategy Considering Power Generation and Consumption Interaction

A New Evolutionary Multiobjective Model for Traveling Salesman Problem

Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests

Contact Info

Product

Resources

About