Thermodynamic analysis and performance optimization of irreversible Carnot refrigerator by using multi-objective evolutionary algorithms (MOEAs)

2015 ◽  
Vol 51 ◽  
pp. 1055-1070 ◽  
Author(s):  
Mohammad H. Ahmadi ◽  
Mohammad Ali Ahmadi ◽  
Seyed Abbas Sadatsakkak
Keyword(s):  
Evolutionary Algorithms ◽  
Thermodynamic Analysis ◽  
Performance Optimization ◽  
Multi Objective ◽  
And Performance

scholarly journals A Pareto-based genetic algorithm for multi-objective scheduling of automated manufacturing systems

2020 ◽  
Vol 12 (1) ◽  
pp. 168781401988529 ◽  
Author(s):  
Xin Zan ◽  
Zepeng Wu ◽  
Cheng Guo ◽  
Zhenhua Yu
Keyword(s):  
Genetic Algorithm ◽  
Performance Optimization ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Automated Manufacturing ◽  
Scheduling Problems ◽  
Automated Manufacturing Systems ◽  
Multi Objective ◽  
Automated Manufacturing System ◽  
And Performance

This work focuses on multi-objective scheduling problems of automated manufacturing systems. Such an automated manufacturing system has limited resources and flexibility of processing routes of jobs, and hence is prone to deadlock. Its scheduling problem includes both deadlock avoidance and performance optimization. A new Pareto-based genetic algorithm is proposed to solve multi-objective scheduling problems of automated manufacturing systems. In automated manufacturing systems, scheduling not only sets up a routing for each job but also provides a feasible sequence of job operations. Possible solutions are expressed as individuals containing information of processing routes and the operation sequence of all jobs. The feasibility of individuals is checked by the Petri net model of an automated manufacturing system and its deadlock controller, and infeasible individuals are amended into feasible ones. The proposed algorithm has been tested with different instances and compared to the modified non-dominated sorting genetic algorithm II. The experiment results show the feasibility and effectiveness of the proposed algorithm.

Generalized Differential Evolution for Constrained Multi-Objective Optimization

2011 ◽  
pp. 43-75 ◽  
Author(s):  
Saku Kukkonen ◽  
Lampinen Jouni
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
General Purpose ◽  
Optimization Process ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Development Phases ◽  
And Performance ◽  
Generalized Differential

Multi-objective optimization with Evolutionary Algorithms has been gaining popularity recently because its applicability in practical problems. Many practical problems contain also constraints, which must be taken care of during optimization process. This chapter is about Generalized Differential Evolution, which is a general-purpose optimizer. It is based on a relatively recent Evolutionary Algorithm, Differential Evolution, which has been gaining popularity because of its simplicity and good observed performance. Generalized Differential Evolution extends Differential Evolution for problems with several objectives and constraints. The chapter concentrates on describing different development phases and performance of Generalized Differential Evolution but it also contains a brief review of other multi-objective DE approaches. Ability to solve multi-objective problems is mainly discussed, but constraint handling and the effect of control parameters are also covered. It is found that GDE versions, in particular the latest version, are effective and efficient for solving constrained multi-objective problems.

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