Performance Studies on Differential Evolution Algorithm and Shuffled Frog-Leaping Algorithm for Simulated Manufacturing Problems

2016 ◽  
Vol 835 ◽  
pp. 858-863 ◽  
Author(s):  
Lakkana Ruekkasaem ◽  
Pasura Aungkulanon
Keyword(s):  
Differential Evolution ◽  
Performance Studies ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Differential Evolution Algorithm ◽  
Shuffled Frog Leaping Algorithm ◽  
Linear Optimization Problems ◽  
Shuffled Frog Leaping ◽  
Manufacturing Problems ◽  
Maximum Minimum

The real world engineering problems are complex associated with lot of factors. The objective of mathematic models in simulated manufacturing problems are to minimize cost or maximize profits while satisfying the constraints. The purpose of this article was to study two algorithms for testing their efficiency in solving non-linear optimization problems and simulated manufacturing problems. A well-known meta-heuristic approach called Differential Evolution (DE) was compared with Shuffled Frog-leaping Algorithm (SFLA) in term of mean, maximum, minimum, and standard deviation of the solution. SFLA was better than DE in terms of the performance to finding optimal solutions because of the unique process of memeplex, which can increase speed of convergence and find turning parameters.

scholarly journals SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput

2019 ◽  
Vol 2019 ◽  
pp. 1-18
Author(s):  
Hongbo Wang ◽  
Xiaoxiao Zhen ◽  
Xuyan Tu
Keyword(s):  
Cognitive Radio ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Population Diversity ◽  
Experimental Simulation ◽  
Target Space ◽  
Throughput Optimization ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping ◽  
Complex Target

Differential Evolution (abbreviation for DE) is showing many advantages in solving optimization problems, such as fast convergence, strong robustness, and so on. However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is premature to fall into the local minimum. All things contend in beauty in the world; a Shuffled Frog Leaping Algorithm (abbreviation for SFLA) has a strong global ability; unfortunately, its convergence speed is also slow. In order to overcome the shortcoming, this article suggests a Shuffled Frog-leaping Differential Evolution (abbreviation for SFDE) algorithm in a cognitive radio network, which combines Differential Evolution with Shuffled Frog Leaping Algorithm. This proposed method hikes its local searching for a certain number of subgroups, and their individuals join together and share their mutual information among different subgroups, which improves the population diversity and achieves the purpose of fast global search during the whole Differential Evolution. The SFDE is examined by 20 well-known numerical benchmark functions, and those obtained results are compared with four other related algorithms. The experimental simulation in solving the problem of effective throughput optimization for cognitive users shows that the proposed SFDE is effective.

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.

Multirow Intersection Cuts Based on the Infinity Norm

2021 ◽  
Author(s):  
Álinson S. Xavier ◽  
Ricardo Fukasawa ◽  
Laurent Poirrier
Keyword(s):  
Optimization Problems ◽  
Valid Inequalities ◽  
Linear Optimization ◽  
Computational Cost ◽  
General Purpose ◽  
Mixed Integer ◽  
Infinity Norm ◽  
Intersection Cuts ◽  
Linear Optimization Problems ◽  
Mixed Integer Linear Optimization

When generating multirow intersection cuts for mixed-integer linear optimization problems, an important practical question is deciding which intersection cuts to use. Even when restricted to cuts that are facet defining for the corner relaxation, the number of potential candidates is still very large, especially for instances of large size. In this paper, we introduce a subset of intersection cuts based on the infinity norm that is very small, works for relaxations having arbitrary number of rows and, unlike many subclasses studied in the literature, takes into account the entire data from the simplex tableau. We describe an algorithm for generating these inequalities and run extensive computational experiments in order to evaluate their practical effectiveness in real-world instances. We conclude that this subset of inequalities yields, in terms of gap closure, around 50% of the benefits of using all valid inequalities for the corner relaxation simultaneously, but at a small fraction of the computational cost, and with a very small number of cuts. Summary of Contribution: Cutting planes are one of the most important techniques used by modern mixed-integer linear programming solvers when solving a variety of challenging operations research problems. The paper advances the state of the art on general-purpose multirow intersection cuts by proposing a practical and computationally friendly method to generate them.

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