Modified Harmony Search Algorithm for 0-1 Knapsack Problems

2013 ◽  
Vol 365-366 ◽  
pp. 182-185
Author(s):  
Hong Gang Xia ◽  
Qing Liang Wang
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Knapsack Problems ◽  
Convergence And Stability ◽  
Update Strategy ◽  
Harmony Memory

In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS).

A Self-Study Harmony Search Algorithm for Optimization Problems

2014 ◽  
Vol 1006-1007 ◽  
pp. 1017-1020
Author(s):  
Ping Zhang ◽  
Mei Ling Li ◽  
Qian Han ◽  
Guo Jun Li
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Optimization Problems ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Unconstrained Optimization Problems ◽  
Self Study ◽  
Harmony Memory

A self-study harmony search (SSHS) algorithm for solving unconstrained optimization problems has presented in this paper . SSHS employs a novel self-study strategy to generate new solution vectors which can enhance accuracy and convergence rate of harmony search (HS) algorithm. SSHS algorithm as proposed, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. a large number of experiments improved that SSHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS)

Improved Global Harmony Search Algorithm for Numerical Optimization

2014 ◽  
Vol 587-589 ◽  
pp. 2295-2298
Author(s):  
Ping Zhang ◽  
Mei Ling Li ◽  
Qian Han ◽  
Yi Ning Zhang ◽  
Guo Jun Li
Keyword(s):  
Convergence Rate ◽  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Recent Literature ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Convergence And Stability ◽  
Benchmark Experiment ◽  
Pitch Adjustment

To intend to improve the optimization performance of harmony search (HS) algorithm, an improved global harmony search (IGHS) algorithm was presented in this paper. In this algorithm, inspired by swarm intelligence, the global best harmony are borrowed to enhance the optimization accuracy of HS; and mutation and crossover operation instead of pitch adjustment operation to improved the algorithm convergence rate. The key parameters are adjusted to balance the local and global search. Several benchmark experiment simulations, the IGHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its other three improved algorithms (IHS, GHS and SGHS) that reported in recent literature.

A Modified Harmony Search Algorithm for Numerical Optimization Problems

2014 ◽  
Vol 989-994 ◽  
pp. 2532-2535
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Numerical Optimization ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
The Other ◽  
Self Learning ◽  
Harmony Memory

This paper presents a modified harmony search (MHS) algorithm for solving numerical optimization problems. MHS employs a novel self-learning strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In the proposed MHS algorithm, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. The other two key parameters PAR and bw adjust dynamically with generation number. Based on a large number of experiments, MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and GHS).

An Improved Novel Global Harmony Search Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2169-2172
Author(s):  
Zhi Kong ◽  
Guo Dong Zhang ◽  
Li Fu Wang
Keyword(s):  
Convergence Rate ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Global Optimum ◽  
Harmony Search Algorithm ◽  
Test Functions ◽  
Benchmark Test ◽  
Novel Method ◽  
Benchmark Test Functions

This paper develops an improved novel global harmony search (INGHS) algorithm for solving optimization problems. INGHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of novel global harmony search (NGHS) algorithm. Simulations for five benchmark test functions show that INGHS possesses better ability to find the global optimum than that of harmony search (HS) algorithm. Compared with NGHS and HS, INGHS is better in terms of robustness and efficiency.

The Use of Factorial Design to Improve a Harmony Search Algorithm to Synthesize Heat-Integrated Distillation Sequences

2018 ◽  
Vol 777 ◽  
pp. 218-225
Author(s):  
Somboon Sukpancharoen ◽  
Thongchai Rohitatisha Srinophakun ◽  
Jongjit Hirunlabh ◽  
Nopporn Rattanachoung
Keyword(s):  
Objective Function ◽  
Factorial Design ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Mixed Integer ◽  
Integer Number ◽  
Flow Sheet ◽  
Stochastic Algorithm

Optimization problems often involve a large number of design variables, and the exact influence of each of these variables upon the objective function can become rather complex; there may exist local optima for the objective function, but for the typical heat-integrated distillation sequence, the matter of interest is solely the global optimum. Therefore, it is necessary to create a stochastic algorithm method which can synthesize distillation systems with multiple components. The encoding process employs and integer number series which allows the system flow sheet structure to be portrayed and then managed. Within this portrayal, the broad synthesis problem takes the form of an implicit MILP (mixed-integer linear programming) problem. This study considers the attributes of six well-known optimization algorithms: Harmony Search algorithm (HS), Artificial Bee Colony (ABC), Bat Algorithm (BA), Crow Search Optimization (CSO), Grew Wolf Optimization (GWO) and Monarch Butterfly Optimization (MBO). The optimal variables which influence the harmony search algorithm can be determined through full factorial design analysis. These variables can then be employed in the search to discover the optimal heat-integrated distillation sequence. The study investigates the attributes of the optimal configuration solution, in terms of harmony size (HS), required number of iterations, harmony memory considering the rate (HMCR), and pitch adjustment rate (PAR). The study then adopts the HS algorithm which is duly improved in order to address the problem. In comparison with alternative techniques, HS is more effective and more robust than other approaches.

The Lot-Streaming Flow Scheduling Shops Based on a Hybrid Discrete Harmony Search Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 563-568
Author(s):  
Hong Yan Han
Keyword(s):  
Flow Shop ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Lot Streaming ◽  
Discrete Harmony Search ◽  
Harmony Memory ◽  
Streaming Flow

To solve the lot-streaming flow shop scheduling problem with the objective to minimize the total weighted earliness and tardiness, a hybrid discrete harmony search (HDHS) algorithm is proposed in this paper. Firstly, an effective harmony memory initialization approach is presented,an initial solution in harmony memory is generated by means of the famous NEH heuristic. Secondly, the HDHS algorithm utilizes an effective improvisation mechanism to generate new harmonies represented by job permutations. Lastly, the insert neighborhood search and swap operator are designed and embedded in the algorithm to enhance the local exploitation.Experimental results demonstrate the effectiveness of the proposed HDHS algorithms.

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.

scholarly journals A Cooperative Harmony Search Algorithm for Function Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Gang Li ◽  
Qingzhong Wang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cooperative Behavior ◽  
Optimization Technique ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Function Optimization ◽  
Solution Vector ◽  
Original Algorithm ◽  
Harmony Memory

Harmony search algorithm (HS) is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs) and particle swarm optimizers (PSOs). It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS) is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.

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