A hybrid approach using TOPSIS, Differential Evolution, and Tabu Search to find multiple solutions of constrained non-linear integer optimization problems

2014 ◽  
Vol 62 ◽  
pp. 47-56 ◽  
Author(s):  
Erick R.F.A. Schneider ◽  
Renato A. Krohling
Keyword(s):  
Tabu Search ◽  
Differential Evolution ◽  
Multiple Solutions ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Integer Optimization ◽  
Non Linear

scholarly journals Solving Transmission Expansion Planning Problem by Enactmenting Differential Examination

2019 ◽  
Vol 9 (2S2) ◽  
pp. 637-641
Keyword(s):  
Power Systems ◽  
Differential Evolution ◽  
Execution Time ◽  
Multiple Solutions ◽  
Optimization Problems ◽  
Planning Problem ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
De Algorithm ◽  
Transmission Expansion

At present the power systems involve extensive and composite unified transmission structures, for substantial issues. It is an extensive, multifaceted and nonlinear problem with multiple solutions to be estimated upsurges with respect to the size of the system. In this decade, differential evolution (DE) algorithm have been employed by several researchers as it is awfully active in resolving optimization problems. In this study, TEP problem is considered in static arrangement. Moreover, one of the cases of static TEP problem has been studied as without generation resizing. DE has attained results with decent exactness, easiness and acceptable execution time. The simulations have been executed using MATLAB

CO-EVOLUTIONARY HYBRID DIFFERENTIAL EVOLUTION FOR MIXED-INTEGER OPTIMIZATION PROBLEMS

2001 ◽  
Vol 33 (6) ◽  
pp. 663-682 ◽  
Author(s):  
YUNG-CHIEN LIN ◽  
KAO-SHING HWANG ◽  
FENG-SHENG WANG
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Mixed Integer Optimization

scholarly journals Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3329
Author(s):  
Sergey Salihov ◽  
Dmitriy Maltsov ◽  
Maria Samsonova ◽  
Konstantin Kozlov
Keyword(s):  
Differential Evolution ◽  
Genomic Selection ◽  
Optimization Problems ◽  
Snp Markers ◽  
Selection Model ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Mixed Integer Optimization ◽  
Wide Range ◽  
Genomic Selection Model

The solution of the so-called mixed-integer optimization problem is an important challenge for modern life sciences. A wide range of methods has been developed for its solution, including metaheuristics approaches. Here, a modification is proposed of the differential evolution entirely parallel (DEEP) method introduced recently that was successfully applied to mixed-integer optimization problems. The triangulation recombination rule was implemented and the recombination coefficients were included in the evolution process in order to increase the robustness of the optimization. The deduplication step included in the procedure ensures the uniqueness of individual integer-valued parameters in the solution vectors. The developed algorithms were implemented in the DEEP software package and applied to three bioinformatic problems. The application of the method to the optimization of predictors set in the genomic selection model in wheat resulted in dimensionality reduction such that the phenotype can be predicted with acceptable accuracy using a selected subset of SNP markers. The method was also successfully used to optimize the training set of samples for such a genomic selection model. According to the obtained results, the developed algorithm was capable of constructing a non-linear phenomenological regression model of gene expression in developing a Drosophila eye with almost the same average accuracy but significantly less standard deviation than the linear models obtained earlier.

scholarly journals Adaptive Unified Differential Evolution Algorithm for Optimal Operation of Power Systems with Static Security, Transient Stability and SSSC Device

2019 ◽  
Vol 9 (1) ◽  
pp. 2238-2353
Keyword(s):  
Power Systems ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Transient Stability ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Optimal Operation ◽  
Facts Devices ◽  
Non Linear ◽  
Linear Optimization Problems

The environmental degradation and increased power demand has forced modern power systems to operate at the closest stability boundaries. Thereby, the power systems operations mainly focus for the inclusion of transient stability constraints in an optimal power flow (OPF) problem. Algebraic and differential equations are including in non-linear optimization problems formed by the transient stability constrained based OPF problem (TSCOPF). Notably, for a small to large power systems solving these non-linear optimization problems is a complex task. In order to achieve the increased power carrying capacity by a power line, the Flexible AC transmission systems (FACTS) devices provides the best supported means a lot. As a result, even under a network contingency condition, the security of the power system is also highly improved with FACTS devices. The FACTS technology has the potential in controlling the routing of the line power flows and the capability of interconnecting networks making the possibility of trading energy between distant agents. This paper presents a new evolutionary algorithm for solving TSCOPF problems with a FACTS device namely adaptive unified differential evolution (AuDE). The large non-convex and nonlinear problems are solved for achieving global optimal solutions using a new evolutionary algorithm called AuDE. Numerical tests on the IEEE 30-bus 6-generator, and IEEE New England 10-generator, 39-bus system have shown the robustness and effectiveness of the proposed AuDE approach for solving TSCOPF in the presence of a FACTS device such as the SSSC device. Due to the page limitation only 30-bus results are presented.

A New Approach for Large Non-Linear Integer Optimization Suitable for Implementation on a Distributed Collection of Computers

2000 ◽  
Author(s):  
Ronald H. Nickel ◽  
Igor Mikolic-Torreira ◽  
Jon W. Tolle
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Minimum Cost ◽  
Spare Parts ◽  
Integer Optimization ◽  
Aircraft Carrier ◽  
Windows Nt ◽  
Non Linear ◽  
Super Computer

Abstract We present a new methodology called Multi-Indenture, Multi-Echelon Readiness-Based Sparing (MIMERBS) for solving large, non-linear integer optimization problems that arise in determining the retail and wholesale sparing policies that support the aircraft operating from a deployed aircraft carrier. MIMERBS determines the minimum cost mix of spare parts that meets required levels of expected aircraft availability. The size (thousands of variables), the nonlinear relationship between spare parts and aircraft availability, and the requirement that the variables be integers make this problem hard. We provide a concise description of the MIMERBS model and present data to show how it improves on earlier sparing models. This improvement comes at the price of significant computationally complexity, which in turn makes the optimization problem hard to solve. We describe how we integrated an interior point method with a direct search algorithm to solve this optimization problem. This hybrid algorithm is well suited for implementation on a home-made virtual super-computer made up of several dozen Windows NT computers connected by an office LAN. A description of the virtual super-computer is given in a separate paper. We report on three specific cases we solved using the MIMERBS model, having from 1,000 to 8,000 optimization variables.

Sign in / Sign up

Export Citation Format

Share Document