A Multi-stage Evolutionary Algorithm for Solving Complex Function Optimization Problems

2009 ◽  
Author(s):  
Yunhao Li ◽  
Shuting Chen
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Complex Function ◽  
Function Optimization ◽  
Multi Stage

scholarly journals Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

2021 ◽  
Vol 6 (4 (114)) ◽  
pp. 6-14
Author(s):  
Maan Afathi
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Fuzzy Logic Control ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Hybrid Evolutionary Algorithm ◽  
Logic Control

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

Model Selecting PSO-FA Hybrid for Complex Function Optimization

2021 ◽  
Vol 12 (3) ◽  
pp. 215-232
Author(s):  
Heng Xiao ◽  
Toshiharu Hatanaka
Keyword(s):  
Model Selection ◽  
Swarm Intelligence ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Complex Function ◽  
Group Behavior ◽  
Black Box ◽  
Function Optimization ◽  
Selection Strategy ◽  
Natural Group

Swarm intelligence is inspired by natural group behavior. It is one of the promising metaheuristics for black-box function optimization. Then plenty of swarm intelligence algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) have been developed. Since these swarm intelligence models have some common properties and inherent characteristics, model hybridization is expected to adjust a swarm intelligence model for the target problem instead of parameter tuning that needs some trial and error approach. This paper proposes a PSO-FA hybrid algorithm with a model selection strategy. An event-driven trigger based on the personal best update makes each individual do the model selection that focuses on the personal study process. By testing the proposed hybrid algorithm on some benchmark problems and comparing it with a simple PSO, the standard PSO 2011, FA, HFPSO to show how the proposed hybrid swarm averagely performs well in black-box optimization problems.

A Heuristic Algorithm Based on Leadership Strategy: Leader of Dolphin Herd Algorithm (LDHA)

2015 ◽  
Vol 19 (4) ◽  
pp. 491-499 ◽  
Author(s):  
Jianqiang Zhao ◽  
◽  
Kao Ge ◽  
Kangyao Xu
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Complex Function ◽  
Predatory Behavior ◽  
Function Optimization ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Leadership Strategy ◽  
Complex Functions

A heuristic algorithm named the leader of dolphin herd algorithm (LDHA) is proposed in this paper to solve an optimization problem whose dimensionality is not high, with dolphins that imitate predatory behavior. LDHA is based on a leadership strategy. Using the leadership strategy as reference, we have designed the proposed algorithm by simulating the preying actions of dolphin herds. Several intelligent behaviors, such as “producing leaders,” “group gathering,” “information sharing,” and “rounding up prey,” are abstracted by LDHA. The proposed algorithm is tested on 15 typical complex function optimization problems. The testing results reveal that compared with the particle swarm optimization and the genetic algorithms, LDHA has relatively high optimization accuracy and capability for complex functions. Further, it is almost unaffected by the inimicality, multimodality, or dimensions of functions in the function optimization section, which implies better convergence. In addition, ultra-high-dimensional function optimization capabilities of this algorithm were tested using the IEEE CEC 2013 global optimization benchmark. Unfortunately, the proposed optimization algorithm has a limitation in that it is not suitable for ultra-high-dimensional functions.

Research of Parallel Artificial Bee Colony Algorithm Based on MPI

2013 ◽  
Vol 380-384 ◽  
pp. 1430-1433 ◽  
Author(s):  
Ying Sen Hong ◽  
Zhen Zhou Ji ◽  
Chun Lei Liu
Keyword(s):  
Parallel Algorithm ◽  
Message Passing ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Complex Function ◽  
Function Optimization ◽  
Search Performance ◽  
Bee Colony ◽  
Long Time

Artificial bee colony algorithm is a smart optimization algorithm based on the bees acquisition model. A long time for the search of the artificial bee colony algorithm, in this paper we propose a parallel algorithm of artificial bee colony algorithm (MPI-ABC), with an application of a parallel programming environment MPI, using the programming mode of message passing rewriting the serial algorithm in parallel. Finally, this paper compare both serial and parallel algorithm with testing on complex function optimization problems. The experimental results show that the algorithm is effective to improve the search performance, especially for high-dimensional complex optimization problem.

An Effective and Efficient Hybrid Algorithm Based on HS–IWO for Global Optimization

2017 ◽  
Vol 32 (04) ◽  
pp. 1859006 ◽  
Author(s):  
Aijia Ouyang ◽  
Shuo Peng ◽  
Xuyu Peng ◽  
Qian Wang
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Harmony Search ◽  
Complex Function ◽  
Function Optimization ◽  
Invasive Weed ◽  
Local Optima ◽  
Intelligent Optimization Algorithms ◽  
Complex Functions ◽  
Limit Solutions

Considering that the invasive weed optimization (IWO) algorithm and the harmony search (HS) algorithm are inclined to fall into local optima with low convergence precision when they are used to deal with complex function optimization problems, this paper proposes a hybrid algorithm, HS–IWO algorithm, which is combined HS algorithm and IWO algorithm. We introduce strategies such as fixing the number of seeds, reinitializing limit solutions, multi-individual global HS, parameter optimization, etc. In order to make the two algorithms take advantage of their merits, they are mixed organically in this paper. Through tests on some complex functions of benchmark, the experimental results display that the HS–IWO algorithm has the efficiency and robustness of the algorithms. It is an optimization algorithm that is highly effective and stable, especially to be applied to the optimization of complicated functions compared with other intelligent optimization algorithms.

Hybrid Dynamical Evolutionary Algorithm and Time Complexity Analysis

2012 ◽  
Vol 263-266 ◽  
pp. 2344-2348
Author(s):  
Hui Ying Li ◽  
Yi Lai Zhang ◽  
Xing Xu
Keyword(s):  
Evolutionary Algorithm ◽  
Time Complexity ◽  
Complexity Analysis ◽  
Optimization Problems ◽  
Function Optimization ◽  
Numerical Function ◽  
Similar Time ◽  
Local Optima ◽  
Minimization Problems ◽  
Dynamical Evolutionary Algorithm

The dynamical evolutionary algorithm (DEA) is a new evolutionary algorithm based on the theory of statistical mechanics, however, DEA converges slowly and often converge at local optima for some function optimization problems. In this paper, a hybrid dynamical evolutionary algorithm (HDEA) with multi-parent crossover and differential evolution mutation is proposed for accelerating convergence velocity and easily escaping suboptimal solutions. Moreover, the population of HDEA is initialized by chaos. In order to confirm the effectiveness of our algorithm, HDEA is applied to solve the typical numerical function minimization problems. The computational complexity of HDEA is analyzed, and the experimental results show that HDEA outperforms the DEA in the aspect of convergence velocity and precision, even the two algorithms have the similar time complexity.

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