Multi-Objective Differential Evolution Algorithm for Solving Engineering Problems

Adeyemo, Josiah; Otieno, Fred

doi:10.3923/jas.2009.3652.3661

Cited by 33 publications

(14 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…89.000 In (24), ( MF , 1 , 2 , ) implicates the model of grinding and classification represented by (12)- (23). The proposed algorithm is applied to solve the problem, and the optimization results are shown in Table 6.…”

Section: Optimization Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification

Wang

Chen

Gui

et al. 2013

Journal of Applied Mathematics

View full text Add to dashboard Cite

The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE) algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS), the satisfactory solution is obtained by using a decision-making method for multiple attributes.

show abstract

Section: Optimization Resultsmentioning

confidence: 99%

“…They further extended MOSADE by using objectivewise learning strategies [22]. Adeyemo and Otieno [23] provided multiobjective differential evolution algorithm (MDEA). In MDEA, a new solution was generated by DE variant and compared with target solution.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification

Wang

Chen

Gui

et al. 2013

Journal of Applied Mathematics

View full text Add to dashboard Cite

show abstract

“…Mathematical programming techniques have certain limitations when tackling multiobjective optimizations (MOPs), such as most of them cannot find multiple solutions in a single run, and the multiple application of these methods does not guarantee finding widely different Pareto-optimal solutions [26][27][28]. However, EAs deal simultaneously with a set of possible solutions that allows for the finding of several members of the Paretooptimal set in a single run of the algorithm [29].…”

Section: Multiobjective Differential Evolution-based Methodsmentioning

confidence: 99%

Multi-Objective Differential Evolution Based PerformanceOptimisation for Switched Reluctance Motor Drives

Yahia¹,

Liouane²,

Dhifaoui³

2013

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

The simple structure, low manufacturing cost, rugged behavior, high torque per unit volume, and wide torque-speed range make a switched reluctance motor (SRM) very attractive for industrial applications. However, these advantages are overshadowed by its inherent high torque ripple, acoustic noise, and difficulty to control. The controlled parameters in SRM drives can be selected as the turn-on angle, the turn-off angle, and the current reference. This paper investigates the problem of optimal control parameters considering the maximum average torque, minimum copper losses, and minimum torque ripple as the main objectives in SRM drives. The use of evolutionary algorithms (EAs) to solve problems with multiple objectives has attracted much attention recently. Differential evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. A multiobjective DE (MODE) technique is introduced here to find the optimal firing angles under multiple operating conditions. The simulation results carried out on a 4-phase 8/6 pole SRM show that the proposed MODE can be a reliable alternative for generating optimal control in the multiobjective optimization of SRM drive systems.

show abstract

“…The detailed description about MODE algorithm and its 5 different test results has been presented . The application of MODE algorithm for solving engineering optimization problems has been presented . A brief description of NSGA‐II and its implementation to GEP has been provided …”

Section: Description About Mode Algorithm and Its Implementation To Mmentioning

confidence: 99%

Multistage multiobjective electricity generation expansion planning for Tamil Nadu considering least cost and minimal GHG emission

Bhuvanesh¹,

Christa²,

Subramanian³

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Developing countries like India necessitates installation of new power plants to meet the elevating electricity demand. Several single‐objective mathematical formulations are modeled and solved previously for short‐term and long‐term power generation expansion planning aiding power system planners to ensure optimal decision. The input data, such as forecasted demands, economic and technical data of the generating units are numerous and unmanageable to be analyzed manually. Modern power systems have faced major challenge in solving multiple objectives such as least cost and minimal greenhouse gas emissions simultaneously. This paper presents an application of multiobjective differential evolution algorithm to multistage, multiobjective generation expansion planning problem for Tamil Nadu, a state in India. The multiobjective generation expansion planning problem has been solved for short‐term (6 years) and long‐term (12 years) planning horizon by considering 8 different cases and also compared with Nondominated Sorting Genetic Algorithm version II.

show abstract

Multi-Objective Differential Evolution Algorithm for Solving Engineering Problems

Cited by 33 publications

References 8 publications

A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification

A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification

Multi-Objective Differential Evolution Based PerformanceOptimisation for Switched Reluctance Motor Drives

Multistage multiobjective electricity generation expansion planning for Tamil Nadu considering least cost and minimal GHG emission

Contact Info

Product

Resources

About