An adaptive resistance and stamina strategy-based dragonfly algorithm for solving engineering optimization problems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yongliang Yuan ◽  
Shuo Wang ◽  
Liye Lv ◽  
Xueguan Song
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Optimal Structure ◽  
Content Type ◽  
Dragonfly Algorithm ◽  
Adaptive Resistance ◽  
Practical Engineering ◽  
Linear Optimization Problems

Purpose Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA). Design/methodology/approach To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies. Findings The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014’s special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design. Originality/value A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.

scholarly journals Performance and Area Optimization of VLSI Systems Using Genetic Algorithms

VLSI Design ◽  
1995 ◽  
Vol 3 (1) ◽  
pp. 43-51 ◽  
Author(s):  
Xiao-Dong Wang ◽  
Tom Chen
Keyword(s):  
Genetic Algorithms ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Delay Model ◽  
Silicon Area ◽  
Vlsi Chip ◽  
Linear Optimization Problems ◽  
Area Optimization ◽  
The Relationship

A new performance and area optimization algorithm for complex VLSI systems is presented. It is widely believed within the VLSI CAD community that the relationship between delay and silicon area of a VLSI chip is convex. This conclusion is based on a simplified linear RC model to predict gate delays. In the proposed optimization algorithm, a nonlinear, non-RC based transistor delay model was used which resulted in a non-convex relationship between the delay and the silicon area of a VLSI chip. Genetic algorithms are better suited for discrete, non-convex, non-linear optimization problems than traditional calculus-based algorithms. By using the genetic algorithms in the performance and area optimization, we are able to find the optimal values for both delay and silicon area for the ISCAS benchmark circuits.

scholarly journals A Global Optimization Algorithm for Unconstrained Non-linear Optimization Problems Using Chaotic Neuron Map and Golden Number

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 05) ◽  
pp. 1118-1136
Author(s):  
G. Sandhya Rani ◽  
Sarada Jayan
Keyword(s):  
Optimization Algorithm ◽  
Global Minimum ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Golden Section ◽  
Linear Optimization ◽  
Second Stage ◽  
Golden Section Search ◽  
Linear Optimization Problems ◽  
Global Minimum Point

This paper presents aninnovative global multi-variable optimization algorithm using one of the best chaotic sequences, the neuron map, a description of which is also provided in the paper. The algorithm uses neuron map in the first stage to move near the global minimum point, as well as in each iteration of the second stage of local search that is done using the N-dimensional golden section search algorithm. The generation and mapping of the neuron variables to the optimization variables along with the stagewise search for the global minimum is explained conscientiously in the work. Numerical results on some benchmark functions and the comparison with a latest state-of-the-art algorithm ispresented in order to demonstrate the efficiency of the proposed algorithm.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

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.

Sign in / Sign up

Export Citation Format

Share Document