An Improved Multiobjective Algorithm: DNSGA2-PSA

Qu, Dan; Ding, Xunliang; Wang, Hongmei

doi:10.1155/2018/9697104

Cited by 2 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Part and Select Algorithm (PSA) was also proposed to maintain diversity, and the entire algorithm after being integrated into NSGA-II was called Diversity DNSGA2–PSA. Additionally, several researchers have added a local search strategy to NSGA-II [ 24 ]. For example, the study in [ 25 ] proposed Heavy Perturbation (HP)-based NSGA-II.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing

Abdali

Hassan

Aman

et al. 2021

Sensors

View full text Add to dashboard Cite

Fog computing is an emerging technology. It has the potential of enabling various wireless networks to offer computational services based on certain requirements given by the user. Typically, the users give their computing tasks to the network manager that has the responsibility of allocating needed fog nodes optimally for conducting the computation effectively. The optimal allocation of nodes with respect to various metrics is essential for fast execution and stable, energy-efficient, balanced, and cost-effective allocation. This article aims to optimize multiple objectives using fog computing by developing multi-objective optimization with high exploitive searching. The developed algorithm is an evolutionary genetic type designated as Hyper Angle Exploitative Searching (HAES). It uses hyper angle along with crowding distance for prioritizing solutions within the same rank and selecting the highest priority solutions. The approach was evaluated on multi-objective mathematical problems and its superiority was revealed by comparing its performance with benchmark approaches. A framework of multi-criteria optimization for fog computing was proposed, the Fog Computing Closed Loop Model (FCCL). Results have shown that HAES outperforms other relevant benchmarks in terms of non-domination and optimality metrics with over 70% confidence of the t-test for rejecting the null-hypothesis of non-superiority in terms of the domination metric set coverage.

show abstract

Section: Background and Literature Reviewmentioning

confidence: 99%

Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing

Abdali

Hassan

Aman

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Another path to development in this field is by adapting other types of meta-heuristic search optimization algorithms, to work in the multi-objective context. For example in [17], denoted as DNSGA2-PSA, which adopts a new vector-ranking scheme, utilize Part and Select Algorithm (PSA), and implement dominance degree approach (DDA-NS) for non-dominated sorting. Similarly, in [18] a shift-based density estimation (SDE) strategy was integrate with NSGA-II in order to maintain the distribution and convergence of individuals.…”

Section: Introductionmentioning

confidence: 99%

Searching With Direction Awareness: Multi-Objective Genetic Algorithm Based on Angle Quantization and Crowding Distance MOGA-AQCD

Metiaf

Aljeroudi

2019

IEEE Access

View full text Add to dashboard Cite

Multi-objective optimization (MOO) is widely used for solving various engineering real-life problems. Meta-heuristic optimization has been regarded as an effective solution for such problems because it enables the successful examination of a broad range of candidate solutions and the selection of optimal ones. However, there is a high probability of the algorithms becoming ensnared in local minima due to the complex optimization surface and the unlimited number of viable solutions. Therefore, to provide the decision maker with the optimal non-dominated set of solutions, significant improvements must be made to the search process, where the efficient exploration of the population has a vital role in maintaining a good non-dominated solution in evolutionary algorithms. NSGA-II is regarded as the state of the art for the meta-heuristic MOO. NSGA-II and its variants have adopted the concept of crowding distance as a measure that can leverage the characteristics of the distribution of solutions in the search space and provide a highlevel of exploration. However, this method is not sufficient to effectively explore the search space because it ignores the direction of the exploration. In this paper, the angle quantization of solutions is combined with the crowding distance to create the MOGA-AQCD algorithm, which preserves equal exploration in all directions and aims at finding equal distribution of solutions within the search space. This approach is applied to a set of standard benchmark MOO functions. The results show that MOGA-AQCD is superior to NSGA-II and NSGA-III on the most evaluation measures for MOO.

show abstract

An Improved Multiobjective Algorithm: DNSGA2-PSA

Cited by 2 publications

References 39 publications

Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing

Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing

Searching With Direction Awareness: Multi-Objective Genetic Algorithm Based on Angle Quantization and Crowding Distance MOGA-AQCD

Contact Info

Product

Resources

About