A novel optimized cyclic part feeding system with line-integrated supermarkets

2018 ◽  
Vol 233 (3) ◽  
pp. 975-987 ◽  
Author(s):  
Binghai Zhou ◽  
Jiahui Xu
Keyword(s):  
Mixed Model ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Global Optimum ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Fleet Size ◽  
Plant Parts ◽  
Part Feeding

To unify the merits of traditional in-plant parts logistics alternatives such as line stocking and kitting, the concept of line-integrated supermarkets is introduced to improve the part feeding in mixed-model assembly lines. First, the highly interdependent optimization problems of assigning stations and scheduling logistics operators are described, and mathematical models are established with the aim to minimize the fleet size of logistics operators and unit part delivery time as well. Together with particular theorems and lemmas, a nested dynamic programming is presented to obtain global optimum for small-sized instances while a modified harmony search algorithm is constructed for medium- or large-sized instances. Benefit from repeatedly dividing and reconstructing the harmony memory, the computation speed is significantly enhanced. Meanwhile, crossover and mutation operations effectively improve the diversity of solutions to overcome deficiencies such as limited search depth and tendencies to trapping into local optimum. Finally, experimental results validate that the proposed algorithm is of competitive performance in effectiveness and efficiency compared to some other basic or modified meta-heuristics.

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.

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.

A Comparative Study of Global-Best Harmony Search and Bat Algorithms on Optimization Problems

2013 ◽  
Vol 464 ◽  
pp. 352-357
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Computation Time ◽  
Finite Sequence ◽  
Bat Algorithm ◽  
Harmony Search Algorithm ◽  
Response Surfaces ◽  
Sample Mean ◽  
Constrained Problems

The engineering optimization problems are large and complex. Effective methods for solving these problems using a finite sequence of instructions can be categorized into optimization and meta-heuristics algorithms. Meta-heuristics techniques have been proved to solve various real world problems. In this study, a comparison of two meta-heuristic techniques, namely, Global-Best Harmony Search algorithm (GHSA) and Bat algorithm (BATA), for solving constrained optimization problems was carried out. GHSA and BATA are optimization algorithms inspired by the structure of harmony improvisation search process and social behavior of bat echolocation for decision direction. These algorithms were implemented under different natures of three optimization, which are single-peak, multi-peak and curved-ridge response surfaces. Moreover, both algorithms were also applied to constrained engineering problems. The results from non-linear continuous unconstrained functions in the context of response surface methodology and constrained problems can be shown that Bat algorithm seems to be better in terms of the sample mean and variance of design points yields and computation time.

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).

scholarly journals Theoretical and Empirical Analyses of an Improved Harmony Search Algorithm Based on Differential Mutation Operator

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Longquan Yong ◽  
Sanyang Liu ◽  
Jianke Zhang ◽  
Quanxi Feng
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Population Diversity ◽  
Mutation Operator ◽  
Good Convergence ◽  
Improved Harmony Search ◽  
Mean And Variance ◽  
Evolution Algorithm

Harmony search (HS) method is an emerging metaheuristic optimization algorithm. In this paper, an improved harmony search method based on differential mutation operator (IHSDE) is proposed to deal with the optimization problems. Since the population diversity plays an important role in the behavior of evolution algorithm, the aim of this paper is to calculate the expected population mean and variance of IHSDE from theoretical viewpoint. Numerical results, compared with the HSDE, NGHS, show that the IHSDE method has good convergence property over a test-suite of well-known benchmark functions.

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