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.

Harmony Search with Greedy Shuffle for Nurse Rostering

2012 ◽  
Vol 3 (2) ◽  
pp. 22-42 ◽  
Author(s):  
Mohammed A. Awadallah ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Asaju La’aro Bolaji
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Nurse Rostering ◽  
Wide Range ◽  
Nurse Rostering Problem ◽  
Different Types

In this paper, a hybridization of Harmony Search Algorithm (HSA) with a greedy shuffle move is proposed for Nurse Rostering Problem (NRP). NRP is a combinatorial optimization problem that is tackled by assigning a set of nurses with different skills and contracts to different types of shifts, over a pre-determined scheduling period. HSA is a population-based method which mimics the improvisation process that has been successfully applied for a wide range of optimization problems. The performance of HSA is enhanced by hybridizing it with a greedy shuffle move. The proposed method is evaluated using a dataset defined in first International Nurse Rostering Competition (INRC2010). The hybrid HSA obtained the best results of the comparative methods in four datasets.

Two Meta-Heuristics for Solving Unconstrained Optimization Problems and Machinery Problems

2014 ◽  
Vol 1044-1045 ◽  
pp. 1418-1423
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Particle Swarm ◽  
Finite Sequence ◽  
Swarm Optimization ◽  
Machining Optimization

Machining optimization problem aims to optimize machinery conditions which are important for economic settings. The effective methods for solving these problems using a finite sequence of instructions can be categorized into two groups; exact optimization algorithm and meta-heuristic algorithms. A well-known meta-heuristic approach called Harmony Search Algorithm was used to compare with Particle Swarm Optimization. We implemented and analysed algorithms using unconstrained problems under different conditions included single, multi-peak, curved ridge optimization, and machinery optimization problem. The computational outputs demonstrated the proposed Particle Swarm Optimization resulted in the better outcomes in term of mean and variance of process yields.

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).

Improved Harmony Search Algorithm for Solving Optimization Problem with Mixed Discrete Variables

2013 ◽  
Vol 325-326 ◽  
pp. 1485-1488
Author(s):  
Shi Ming Hao ◽  
Li Zhi Cheng
Keyword(s):  
Constrained Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Decision Variables

The classical harmony search algorithm (HSA) can only be used to solve the unconstrained optimization problems with continuous decision variables. Therefore, the classical HSA is not suitable for solving an engineering optimization problem with mixed discrete variables. In order to improve the classical HSA, an engineering method for dealing with mixed discrete decision variables is introduced and an exact non-differentiable penalty function is used to transform the constrained optimization design model into an unconstrained mathematical model. Based on above improvements, a program of improved HSA is designed and it can be used for solving the constrained optimization design problems with continuous variables, integer variables and non-equidistant discrete variables. Finally, an optimization design example of single-stage cylindrical-gear reducer with mixed-discrete variables is given. The example shows that the designed program runs steadily and the proposed method is effective in engineering design.

A New Variant Harmony Search Algorithm for Unconstrained Numerical Optimization Problems

2013 ◽  
Vol 365-366 ◽  
pp. 174-177
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Intelligent Optimization ◽  
New Variant ◽  
Key Parameter

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method and it does depend on imitating the music improvisation process to generate a perfect state of harmony. However, intelligent optimization methods is easily trapped into local optimal, HS is no exception. In order to modify the optimization performance of HS, a new variant of harmony search algorithm is proposed in this paper. The variant integrate the position updating of the particle swarm optimization algorithm with pitch adjustment operation, and dynamically adjust the key parameter pitch adjusting rate (PAR) and bandwidth (BW). Several standard benchmarks are to be tested. The numerical results demonstrated the superiority of the proposed method to the HS and recently developed variants (IHS, and GHS).

scholarly journals TRUE POWER LOSS REDUCTION BY HARMONY SEARCH ALGORITHM

2018 ◽  
Vol 6 (9) ◽  
pp. 196-205
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Test System ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Real Power ◽  
Power Loss Reduction

This paper presents Harmony Search algorithm (HS) for solving the reactive power problem.  Real power loss minimization is the major objective & also voltage profiles are should be kept within the limits.  This paper introduces a new search model the harmony search (HS) algorithm is a relatively new population-based metaheuristic optimization algorithm. It emulates the music improvisation progression where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. In order to evaluate the efficiency of the proposed algorithm, it has been tested on practical 191 test system & real power loss has been considerably reduced.

Modified Harmony Search Algorithm for Unconstrained Numerical Optimization Problems

2014 ◽  
Vol 989-994 ◽  
pp. 2528-2531
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Perfect State ◽  
Evolution Algorithm

Harmony search algorithm is a new meta-heuristic optimization method imitating the music improvisation process where musicians improvise their instruments’ pitches searching for a perfect state of harmony. To enable the harmony search algorithm to transcend its limited capability of local optimum, a modified harmony search algorithm is proposed in this paper. In the modified harmony search algorithm, the mutation operation of differential evolution algorithm is introduced into MHS algorithm, which improves its convergence. Several standard benchmark optimization functions are to be test and compare the performance of the MHS. The results revealed the superiority of the proposed method to the HS and recently developed variants.

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.

An Amended Harmony Search Algorithm for Unconstrained Optimization Problems

2012 ◽  
Vol 190-191 ◽  
pp. 911-914
Author(s):  
Ruo Ping Li ◽  
Hai Bin Ouyang ◽  
Li Qun Gao
Keyword(s):  
Convergence Rate ◽  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Local Optima ◽  
Unconstrained Optimization Problems ◽  
Solution Accuracy

Harmony search (HS) algorithm is a new mate heuristic algorithm, which is conceptualized using the musical improvisation process of searching for a perfect state of harmony. Its own potential and shortage, one of its main disadvantages is that it easily trapped into local optima and converges very slowly. Based on the conception of swarm intelligence, this paper presents an amended harmony search (AHS) algorithm. AHS introduces a novel position updating strategy for generating new solution vectors, which enhances solution accuracy and convergence rate of algorithm. Several standard benchmark optimization functions are to be test and compare the performance of the AHS. The results revealed the superiority of the proposed method to the HS and its three improved algorithms (IHS, GHS and NGHS).

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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