scholarly journals A bat optimization algorithm with moderate orientation and perturbation of trend

2021 ◽  
Vol 15 ◽  
pp. 174830262110084
Author(s):  
Jingsen Liu ◽  
Hongyuan Ji ◽  
Qingqing Liu ◽  
Yu Li
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Random Perturbation ◽  
Bat Algorithm ◽  
Local Extremum ◽  
Global Search ◽  
Deep Mining ◽  
Nonlinear Variation ◽  
Optimal Value ◽  
Search Stage

In order to improve the convergence speed and optimization accuracy of the bat algorithm, a bat optimization algorithm with moderate optimal orientation and random perturbation of trend is proposed. The algorithm introduces the nonlinear variation factor into the velocity update formula of the global search stage to maintain a high diversity of bat populations, thereby enhanced the global exploration ability of the algorithm. At the same time, in the local search stage, the position update equation is changed, and a strategy that towards optimal value modestly is used to improve the ability of the algorithm to local search for deep mining. Finally, the adaptive decreasing random perturbation is performed on each bat individual that have been updated in position at each generation, which can improve the ability of the algorithm to jump out of the local extremum, and to balance the early global search extensiveness and the later local search accuracy. The simulating results show that the improved algorithm has a faster optimization speed and higher optimization accuracy.

scholarly journals Design and performance analysis of a new optimization algorithm based on Finite Element Analysis

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042095085
Author(s):  
Zhiqiang Ning ◽  
Youshan Gao ◽  
Aihong Wang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Solid State ◽  
Local Search ◽  
Optimization Algorithm ◽  
Population Based ◽  
Global Search ◽  
Element Analysis ◽  
State Diffusion ◽  
And Performance

Aiming at the problem that many algorithms could not effectively balance the global search ability and local search ability, a new optimization algorithm is proposed. Inspired by Finite Element Analysis (FEA) approach, a relationship of mapping between Finite Element Analysis approach and a population-based optimization algorithm is constructed through comparing the similarities and differences of FEA node and ideal particle. In algorithm framework, the stiffness coefficient corresponds to a user-defined function of the value of an objective function to be optimized, and the node forces among individuals are defined and an attraction-repulsion rule is established among them. The FEA approach that can simulate multi- states of matter is adopted to balance the global search ability and local search ability in the novel optimization algorithm. A theoretical analysis is made for algorithm parallelism. The conditions for convergence are deduced through analyzing the algorithm based on discrete-time linear system theory. In addition, the performance of the algorithm is compared with PSO for five states which include free state, diffusion state, solid state, entirely solid state, synthesis state. The simulation results of six benchmark functions show that the algorithm is effective. The algorithm supplies a new method to solve optimization problem.

The Improvement of Fruit Fly Optimization Algorithm-Using Bivariable Function as Example

2013 ◽  
Vol 756-759 ◽  
pp. 2952-2957 ◽  
Author(s):  
Fu Qiang Xu ◽  
You Tian Tao
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Nonlinear Function ◽  
Fruit Fly ◽  
Global Optimum ◽  
Local Extremum ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Research Fields ◽  
Optimal Value

Optimization problems are always the hot issues in various research fields. The aim of this paper is to find the optimal value of the bivariable nonlinear function by means of the improved fruit fly optimization algorithm (G-FOA). Some better results are obtained. Compared with other algorithms, G-FOA is concise, can quickly find the global optimum with the high accuracy and without falling into local extremum. These advantages make the algorithm has good robustness and applicability.

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.

scholarly journals GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

2006 ◽  
Vol 14 (2) ◽  
pp. 223-253 ◽  
Author(s):  
Frédéric Lardeux ◽  
Frédéric Saubion ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Hybrid Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Satisfiability Problem ◽  
Local Search Algorithm ◽  
Overall Performance ◽  
Genetic Local Search ◽  
Search Stage

This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.

An Adaptive Invasive Weed Optimization Algorithm

2015 ◽  
Vol 29 (02) ◽  
pp. 1559004 ◽  
Author(s):  
Shuo Peng ◽  
A.-J. Ouyang ◽  
Jeff Jun Zhang
Keyword(s):  
Optimization Algorithm ◽  
Initial Step ◽  
Local Extremum ◽  
Invasive Weed Optimization ◽  
Step Size ◽  
Invasive Weed ◽  
Adaptive Step Size ◽  
Initial Step Size ◽  
Adaptive Step ◽  
Optimization Search

With regards to the low search accuracy of the basic invasive weed optimization algorithm which is easy to get into local extremum, this paper proposes an adaptive invasive weed optimization (AIWO) algorithm. The algorithm sets the initial step size and the final step size as the adaptive step size to guide the global search of the algorithm, and it is applied to 20 famous benchmark functions for a test, the results of which show that the AIWO algorithm owns better global optimization search capacity, faster convergence speed and higher computation accuracy compared with other advanced algorithms.

scholarly journals A New Fruit Fly Optimization Algorithm Based on Differential Evolution

2015 ◽  
Vol 3 (4) ◽  
pp. 365-373 ◽  
Author(s):  
Dabin Zhang ◽  
Jia Ye ◽  
Zhigang Zhou ◽  
Yuqi Luan
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Local Extremum ◽  
Experimental Results ◽  
Fruit Fly Optimization Algorithm ◽  
A Algorithm ◽  
Fruit Fly Optimization ◽  
Searching Ability ◽  
Selection Of

Abstract In order to overcome the problem of low convergence precision and easily relapsing into local extremum in fruit fly optimization algorithm (FOA), this paper adds the idea of differential evolution to fruit fly optimization algorithm so as to optimizing and a algorithm of fruit fly optimization based on differential evolution is proposed (FOADE). Adding the operating of mutation, crossover and selection of differential evolution to FOA after each iteration, which can jump out local extremum and continue to optimize. Compared to FOA, the experimental results show that FOADE has the advantages of better global searching ability, faster convergence and more precise convergence.

scholarly journals An evolutionary-based optimization algorithm for truss sizing design

2016 ◽  
Vol 38 (4) ◽  
pp. 307-317
Author(s):  
Pham Hoang Anh
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
The Other ◽  
Truss Structures ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Objective Functions

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

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