scholarly journals Contention-Aware Mapping and Scheduling Optimization for NoC-Based MPSoCs (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13995-13996
Author(s):  
Yupeng Zhou ◽  
Rongjie Yan ◽  
Anyu Cai ◽  
Yige Yan ◽  
Minghao Yin
Keyword(s):  
Evolutionary Algorithm ◽  
Network On Chip ◽  
Multi Objective Optimization ◽  
Scheduling Optimization ◽  
Multi Objective ◽  
Temporal Aspects ◽  
Network Contention ◽  
On Chip ◽  
Constraint Model

We consider spacial and temporal aspects of communication to avoid contention in Network-on-Chip (NoC) architectures. A constraint model is constructed such that the design concerns can be evaluated, and an efficient evolutionary algorithm with various heuristics is proposed to search for better solutions. Experimentations from random benchmarks demonstrate the efficiency of our method in multi-objective optimization and the effectiveness of our techniques in avoiding network contention.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

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