Hybrid Harmony Search Algorithms

2015 ◽  
Vol 24 (06) ◽  
pp. 1530001 ◽  
Author(s):  
Nazmul Siddique ◽  
Hojjat Adeli
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Harmony Search ◽  
Computational Cost ◽  
Population Based ◽  
Search Algorithms ◽  
Harmony Search Algorithm ◽  
Solution Quality

Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. In order to improve exploration or global search ability, exploit local search more effectively, increase convergence speed, improve solution quality, and minimize computational cost, researchers have advanced the concept of hybridizing HSA with other algorithms. This article presents a review of hybrid harmony search algorithms.

Harmony Search Algorithm and its Variants

2015 ◽  
Vol 29 (08) ◽  
pp. 1539001 ◽  
Author(s):  
Nazmul Siddique ◽  
Hojjat Adeli
Keyword(s):  
Heuristic Search ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
The Past

In the past three decades nature-inspired and meta-heuristic algorithms have dominated the literature in the broad areas of search and optimization. Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. The concept behind the algorithm is to find a perfect state of harmony determined by aesthetic estimation. This paper starts with an overview of the harmonic phenomenon in music and music improvisation used by musicians and how it is applied to the optimization problem. The concept of harmony memory and its mathematical implementation are introduced. A review of HSA and its variants is presented. Guidelines from the literature on the choice of parameters used in HSA for effective solution of optimization problems are summarized.

An Efficient Control Implementation for Inverter Based Harmony Search Algorithm

2017 ◽  
Vol 8 (1) ◽  
pp. 279
Author(s):  
Mushtaq Najeeb ◽  
Hamdan Daniyal ◽  
Ramdan Razali ◽  
Muhamad Mansor
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Dynamic Performance ◽  
Harmony Search ◽  
Absolute Error ◽  
Fast Response ◽  
Pi Controller ◽  
Harmony Search Algorithm ◽  
Voltage Source ◽  
Output Performance

This research implements a PI controller based on harmony search (HS) optimization algorithm for voltage source inverter to improve the output performance under step load change conditions. The HS algorithm aims to handle the trial and error procedure used in finding the PI parameters and then apply the proposed control algorithm via the eZdsp TMS320F28355 board to link the inverter prototype with the Matlab Simulink. The mean absolute error (MAE) is used as an optimization problem to minimize the output voltage error for the developed controller (PI-HS) as compared to the PI controller based particale swarm optimization algorithm (PI-PSO). Based on the experimental results obtained, it is noted that the proposed controller (PI-HS) provides a good dynamic performance, robustness, constant voltage amplitude, and fast response in terms of overshoot, transient, and steady-state.

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).

Normal Parameter Reduction in Soft Set Based on Harmony Search Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2173-2176
Author(s):  
Zhi Kong ◽  
Guo Dong Zhang ◽  
Li Fu Wang
Keyword(s):  
Data Mining ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Soft Set ◽  
Intelligent Optimization ◽  
Parameter Reduction ◽  
Intelligent Optimization Algorithm ◽  
Normal Parameter

The normal parameter reduction in soft set is difficult to application in data mining because of great calculation quantity. In this paper, the intelligent optimization algorithm, the harmony search algorithm, is applied to solve the problem. The normal parameter reduction model is constructed and the harmony search algorithm is designed. Experience has shown that the method is feasible and fast..

scholarly journals OPTIMASI PENJADWALAN PRODUKSI MENGGUNAKAN PENDEKATAN ALGORITMA HARMONY SEARCH DI PT ADI SATRIA ABADI (ASA

2020 ◽  
Vol 11 (1) ◽  
pp. 7-12
Author(s):  
Inaaratul Chusna Ichda Purwanto ◽  
Yohanes Anton Nugroho ◽  
Suseno Suseno
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Search Algorithms ◽  
Harmony Search Algorithm ◽  
Due Date ◽  
Average Value ◽  
Scheduling Method ◽  
Time Savings ◽  
A Company ◽  
Selection Of

PT Adi Satria Abadi (ASA) is a company engaged in the processing of leather, especially sheep skin and goat skin, which is used for the manufacture of golf gloves. The problem faced by the company is the production process that exceeds the due date to other customers who order products at PT ASA. From the research, it is known that the cause is a company scheduling method that has not been organized so that the production sequence is concurrent. Selection of methods Harmony Search algorithms in scheduling are caused by delays. The Harmony Search algorithm can provide a better makespan value than the company method. The results of the company method obtain 0.9 months makespan average, the Harmony Search Algorithm method produces an average 0.8 months makespan. In addition, the use of the Harmony Search Algorithm method can reduce the average value of 0.1 months makespan. The results of the study in three months experienced time savings of 0.6 months, 0.6 months and 0.1 months respectively.

A Hybrid Nature-Inspired Artificial Bee Colony Algorithm for Uncapacitated Examination Timetabling Problems

2015 ◽  
Vol 24 (1) ◽  
pp. 37-54 ◽  
Author(s):  
Asaju La’aro Bolaji ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah
Keyword(s):  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Second Phase ◽  
Examination Timetabling ◽  
Bee Colony ◽  
Population Based Algorithm

AbstractThis article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than exiting ABC techniques and competes well with other techniques from the literature.

scholarly journals A Reward Population-Based Differential Genetic Harmony Search Algorithm

Algorithms ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 23
Author(s):  
Yang Zhang ◽  
Jiacheng Li ◽  
Lei Li
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Memory Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Global Search ◽  
Calculation Results ◽  
Optimal Average ◽  
Average Fitness ◽  
Harmony Memory

To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability.

Sign in / Sign up

Export Citation Format

Share Document