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.

Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms

2019 ◽  
Vol 234 (5) ◽  
pp. 1472-1479 ◽  
Author(s):  
Tey Jing Yuen ◽  
Rahizar Ramli
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Time Frame ◽  
Motor Power ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Multi Objective

A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. The electric formula vehicle has a maximum combined motor power of 80 kW, which is a constraint for delivering maximum vehicle performance with minimal energy consumption. The performance of the vehicle will be simulated and measured against different driving events, that is, acceleration event, autocross event, and endurance event. Each event demands a different aspect of performance to be delivered by the motor. The respective event lap time or energy rating will be measured for performance assessment. In this study, a non-dominated sorting genetic algorithm II and constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution are employed to optimize the motor transmission ratio, motor torque scaling, and downforce scale of both front and rear wheels against the acceleration event to minimize energy consumption and event lap time while constraining the combined motor power of all wheels to not exceed 80 kW. The optimization will be performed through software-in-the-loop between MATLAB and VI-Grade, where the high-fidelity vehicle will be modeled in VI-Grade and optimization algorithms will be implemented on the host in MATLAB. Results show that the non-dominated sorting genetic algorithm II outperforms the constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution in obtaining a wider distributed Pareto solution and converges at a relatively shorter time frame. The optimized results show a promising increase in the performance of the electric formula vehicle in completing those events with the highest combined performance scoring, that is, the lap time of acceleration events improves by 9.18%, that of autocross event improves by 6.1%, and that of endurance event improves by 4.97%, with minimum decrease in energy rating of 32.54%.

scholarly journals A MULTI-OBJECTIVE OPTIMIZATION OF AN ULTRA-WIDEBAND ANTENNA USING AN EVOLUTIONARY ALGORITHM

2021 ◽  
Vol 16 (7) ◽  
Author(s):  
Atif Sardar Khan
Keyword(s):  
Electrical Conductivity ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Ultra Wideband ◽  
Multi Objective Optimization ◽  
Wideband Antenna ◽  
Circular Patch ◽  
Multi Objective ◽  
Textile Antenna ◽  
Tan Δ

In this research, a unique textile antenna is reported for ultra-wideband applications. The material used for the ground and patch of an antenna is conductive woven zelt and the substrate of the antenna is made of cotton (Tan δ = 0.02, εr = 1.54). The suggested antenna is made of a circular patch of a miniature size i.e. 20 mm × 16.922 mm × 2 mm. The zelt is 0.03 mm thick, bearing electrical conductivity up to 0.01 Ω/m. The antenna bandwidth and gain are optimized by using a multi-objective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE). The gain and bandwidth are improved to 4.9 dBi and 2.8 GHz to 15 GHz, respectively. The suggested antenna can be used for Wifi, GPS, and ultra-wideband operations.

scholarly journals An Improved Multi-Objective Evolutionary Approach for Aerospace Shell Production Scheduling Problem

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 509
Author(s):  
Qing Wang ◽  
Xiaoshuang Wang ◽  
Haiwei Luo ◽  
Jian Xiong
Keyword(s):  
Evolutionary Algorithm ◽  
Production Scheduling ◽  
Production Line ◽  
Optimization Problems ◽  
Structural Features ◽  
Optimization Process ◽  
Scheduling Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Shell Production

To certain degree, multi-objective optimization problems obey the law of symmetry, for instance, the minimum of one objective function corresponds to the maximum of another objective. To provide effective support for the multi-objective operation of the aerospace product shell production line, this paper studies multi-objective aerospace shell production scheduling problems. Firstly, a multi-objective optimization model for the production scheduling of aerospace product shell production lines is established. In the presented model, the maximum completion time and the cost of production line construction are optimized simultaneously. Secondly, to tackle the characteristics of discreteness, non-convexity and strong NP difficulty of the multi-objective problem, a knowledge-driven multi-objective evolutionary algorithm is designed to solve the problem. In the proposed approach, structural features of the scheduling plan are extracted during the optimization process and used to guide the subsequent optimization process. Finally, a set of test instances is generated to illustrate the addressed problem and test the proposed approach. The experimental results show that the knowledge-driven multi-objective evolutionary algorithm designed in this paper has better performance than the two classic multi-objective optimization methods.

Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems

2012 ◽  
pp. 75-101
Author(s):  
Sotirios K. Goudos
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Design Problems ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Microwave Design ◽  
Generalized Differential

Antenna and microwave design problems are, in general, multi-objective. Multi-objective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. Particle Swarm Optimization (PSO) and Differential Evolution (DE) have received increased interest from the electromagnetics community. The fact that both algorithms can efficiently handle arbitrary optimization problems has made them popular for solving antenna and microwave design problems. This chapter presents three different state-of-the-art MOEAs based on PSO and DE, namely: the Multi-objective Particle Swarm Optimization (MOPSO), the Multi-objective Particle Swarm Optimization with fitness sharing (MOPSO-fs), and the Generalized Differential Evolution (GDE3). Their applications to different design cases from antenna and microwave problems are reported. These include microwave absorber, microwave filters and Yagi-uda antenna design. The algorithms are compared and evaluated against other evolutionary multi-objective algorithms like Nondominated Sorting Genetic Algorithm-II (NSGA-II). The results show the advantages of using each algorithm.

The Multi-Objective Differential Evolutionary Algorithms and its Application in Optimal Allocation of Water Resources

2012 ◽  
Vol 518-523 ◽  
pp. 4093-4096
Author(s):  
Yan Hong Long ◽  
Li Yang Yu
Keyword(s):  
Water Resources ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Swarm Intelligence ◽  
Optimal Allocation ◽  
Differential Evolution Algorithm ◽  
Multi Objective ◽  
Evolution Algorithm ◽  
Optimization Search

Abstract: Differential evolution algorithm (differential evolution DE) is a multi-objective evolutionary algorithm based on groups， which instructs optimization search by swarm intelligence produced by co-operation and competition among individuals within groups. This paper presents it to the research of optimal allocation of water resources. Accord to the application of the example, the results shows that reasonable and effective.

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