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.

An Efficient Hybrid Algorithm Based on HS and SFLA

2016 ◽  
Vol 30 (05) ◽  
pp. 1659012 ◽  
Author(s):  
Aijia Ouyang ◽  
Xuyu Peng ◽  
Yanbin Liu ◽  
Lilue Fan ◽  
Kenli Li
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Harmony Search ◽  
Complex Function ◽  
Continuous Optimization ◽  
Function Optimization ◽  
Local Optima ◽  
Hybrid Mechanism ◽  
Complex Functions ◽  
Continuous Optimization Problems

When used for optimizing complex functions, harmony search (HS) and shuffled frog leaping algorithm (SFLA) algorithm tend to easily get trapped into local optima and result in low convergence precision. To overcome such shortcomings, a hybrid mechanism of selective search by combining HS algorithm and SFLA algorithm is as well proposed. An HS-SFLA algorithm is designed by taking the advantages of HS and SFLA algorithms. The hybrid algorithm of HS-SFLA is adopted for dealing with complex function optimization problems, the experimental results show that HS-SFLA outperforms other state-of-the-art intelligence algorithms significantly in terms of global search ability, convergence speed and robustness on 80% of the benchmark functions tested. The HS-SFLA algorithm could directly be applied to all kinds of continuous optimization problems in the real world.

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.

scholarly journals An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaobing Yu ◽  
Jie Cao ◽  
Haiyan Shan ◽  
Li Zhu ◽  
Jun Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Stochastic Search ◽  
Adaptive Mutation ◽  
Swarm Optimization ◽  
Local Optima

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.

Hybrid Harmony Search Algorithm for Global Optimization Problems

2014 ◽  
Vol 1065-1069 ◽  
pp. 3438-3441
Author(s):  
Guo Jun Li
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Search Method ◽  
Local Optima ◽  
Population Based Algorithm ◽  
Better Than

Harmony search (HS) algorithm is a new population based algorithm, which imitates the phenomenon of musical improvisation process. Its own potential and shortage, one shortage is that it easily trapped into local optima. In this paper, a hybrid harmony search algorithm (HHS) is proposed based on the conception of swarm intelligence. HHS employed a local search method to replace the pitch adjusting operation, and designed an elitist preservation strategy to modify the selection operation. Experiment results demonstrated that the proposed method performs much better than the HS and its improved algorithms (IHS, GHS and NGHS).

A PARALLEL METAHEURISTIC FRAMEWORK BASED ON HARMONY SEARCH FOR SCHEDULING IN DISTRIBUTED COMPUTING SYSTEMS

2012 ◽  
Vol 23 (02) ◽  
pp. 445-464 ◽  
Author(s):  
YOUNG CHOON LEE ◽  
JAVID TAHERI ◽  
ALBERT Y. ZOMAYA
Keyword(s):  
Optimization Problems ◽  
Harmony Search ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Local Optima ◽  
Computer Clusters ◽  
Parallel Metaheuristics ◽  
Actual Solution ◽  
Parallel Hybrid ◽  
The Coordinator

A large number of optimization problems have been identified as computationally challenging and/or intractable to solve within a reasonable amount of time. Due to the NP-hard nature of these problems, in practice, heuristics account for the majority of existing algorithms. Metaheuristics are one very popular type of heuristics used for many of these optimization problems. In this paper, we present a novel parallel-metaheuristic framework, which effectively enables to devise parallel metaheuristics, particularly with heterogeneous metaheuristics. The core component of the proposed framework is its harmony-search-based coordinator. Harmony search is a recent breed of metaheuristic that mimics the improvisation process of musicians. The coordinator facilitates heterogeneous metaheuristics (forming a parallel metaheuristic) to escape local optima. Specifically, best solutions generated by these worker metaheuristics are maintained in the harmony memory of the coordinator, and they are used to form new-possibly better-harmonies (solutions) before actual solution sharing between workers occurs; hence, their solutions are harmonized with each other. For the applicability validation and the performance evaluation, we have implemented a parallel hybrid metaheuristic using the framework for the task scheduling problem on multiprocessor computing systems (e.g., computer clusters). Experimental results verify that the proposed framework is a compelling approach to parallelize heterogeneous metaheuristics.

An improved differential harmony search algorithm for function optimization problems

2018 ◽  
Vol 23 (13) ◽  
pp. 4827-4852 ◽  
Author(s):  
Lin Wang ◽  
Huanling Hu ◽  
Rui Liu ◽  
Xiaojian Zhou
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Function Optimization
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