A Multi-Objective Evolutionary Algorithm based on complete-linkage clustering to enhance the solution space diversity

2012 ◽  
Author(s):  
Kamyab Tahernezhad ◽  
Kimia Bazargan Lari ◽  
Ali Hamzeh ◽  
Sattar Hashemi
Keyword(s):  
Evolutionary Algorithm ◽  
Solution Space ◽  
Space Diversity ◽  
Complete Linkage ◽  
Multi Objective

A MULTI-OBJECTIVE HW–SW CO-SYNTHESIS ALGORITHM BASED ON QUANTUM-INSPIRED EVOLUTIONARY ALGORITHM

2008 ◽  
Vol 07 (02) ◽  
pp. 129-148 ◽  
Author(s):  
WENLONG WEI ◽  
BIN LI ◽  
YI ZOU ◽  
WENCONG ZHANG ◽  
ZHENQUAN ZHUANG
Keyword(s):  
Embedded System ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Solution Space ◽  
Quantum Probability ◽  
Multiple Quantum ◽  
Synthesis Algorithm ◽  
Multi Objective ◽  
Key Steps ◽  
Slot Filling

Hardware–Software (HW–SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW–SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processing of tasks to achieve a good balance among performance, cost, power consumption, etc. Hence, it is a typical multi-objective optimization problem. In this paper, a new multi-objective HW–SW co-synthesis algorithm based on the quantum-inspired evolutionary algorithm (MQEAC) is proposed. MQEAC utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Meanwhile, this paper presents a new crossover operator to accelerate the convergence to the Pareto front and introduces a PE slot-filling strategy to improve the efficiency of scheduling. Experimental results show that the proposed algorithm can solve the typical multi-objective co-synthesis problems effectively and efficiently.

scholarly journals A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

2022 ◽  
Author(s):  
Federico Antonello ◽  
Piero Baraldi ◽  
Enrico Zio ◽  
Luigi Serio
Keyword(s):  
Evolutionary Algorithm ◽  
Large Scale ◽  
State Of The Art ◽  
Solution Space ◽  
Nuclear Research ◽  
Functional Dependencies ◽  
Direct Identification ◽  
European Organization ◽  
Local Optima ◽  
Multi Objective

AbstractIn this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

Multi-objective evolutionary algorithm based on neighborhood

2008 ◽  
Vol 28 (6) ◽  
pp. 1570-1574
Author(s):  
Mi-qing LI ◽  
Jin-hua ZHENG ◽  
Biao LUO ◽  
Jun WU ◽  
Shi-hua WEN
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective
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