Improving Exploration And Exploitation Capability Of Harmony Search Algorithm

Harmony Search (HS) is a meta-heuristic algorithm which was first introduced in 2001 and it became a widely used optimization algorithm in various areas in engineering application as well as in water resources planning and management. However, as most meta-heuristic algorithms are, the HS shows a good performance in global search but not as good in local search. This study aims the improvement of both exploration and exploitation capability of the algorithm. The mission has been carried out by changing algorithm operators or parameters in the search process. Several types of Improved Harmony Search (IHS) have been successfully developed resulting better exploiting (local) search. Alternative way is to utilize the superior local search of other models or algorithms. The combined, so called hybrid algorithms can significantly supplement the weak local search aspect of the original HS. A newly developed hybrid algorithm, Smallest Small World Cellular Harmony Search (SSWCHS), is developed and proposed shorter characteristic path length and higher clustering coefficient, resulting good exploration and exploitation efficiency. Application to benchmark functions and design of pipe networks proves the superior performance of the newly developed hybrid algorithm

An improved block matching algorithm for motion estimation invideo sequences and application in robotics

Block Matching is one of the most efficient techniques for motion estimation for video sequences. Metaheuristic algorithms have been used effectively for motion estimation. In this paper, we propose two hybrid algorithms: Artificial Bee Colony with Differential Evolution and Harmony Search with Differential Evolution based motion estimation algorithms. Extensive experiments are conducted using four standard video sequences. The video sequences utilized for experimentation have all essential features such as different formats, resolutions and number of frames which are generally required in input video sequences. We compare the performance of the proposed algorithms with other algorithms considering various parameters such as Structural Similarity, Peak Signal to Noise Ratio, Average Number of Search Points etc. The comparative results demonstrate that the proposed algorithms outperformed other algorithms

An improved block matching algorithm for motion estimation in video sequences and application in robotics

Block Matching is one of the most efficient techniques for motion estimation for video sequences. Metaheuristic algorithms have been used effectively for motion estimation. In this paper, we propose two hybrid algorithms: Artificial Bee Colony with Differential Evolution and Harmony Search with Differential Evolution based motion estimation algorithms. Extensive experiments are conducted using four standard video sequences. The video sequences utilized for experimentation have all essential features such as different formats, resolutions and number of frames which are generally required in input video sequences. We compare the performance of the proposed algorithms with other algorithms considering various parameters such as Structural Similarity, Peak Signal to Noise Ratio, Average Number of Search Points etc. The comparative results demonstrate that the proposed algorithms outperformed other algorithms

Hybrid harmony search algorithm for global optimization

Abstract—This paper proposes two hybrid optimization methods based on Harmony Search algorithm (HS) and two different nature-inspired metaheuristic algorithms. In the first contribution, the combination was between the Improved Harmony Search (IHS) and the Particle Swarm Optimization (PSO). The second contribution merged the IHS with the Differential Evolution (DE) operators. The basic idea of hybridization was to ameliorate all the harmony memory vectors by adapting the PSO velocity or the DE operators in order to increase the convergence speed. The new algorithms (IHSPSO and IHSDE) have been compared to the IHS, DE, PSO and some other algorithms like DHS and HSDM. The DHS and HSDM are two existing algorithms, which use different hybridization concepts between HS and DE. All of these algorithms have been evaluated by different test Benchmark functions. The results demonstrated that the hybrid algorithm IHSDE have the better convergence speed into the global optimum than the IHSPSO and the standard IHS, DE and PSO

Optimization of buttressed earth-retaining walls using hybrid harmony search algorithms

This paper represents an economic optimization of buttressed earth-retaining walls. We explore the optimum solutions using a harmony search with an intensification stage through threshold accepting. The calibration of the resulting algorithm has been obtained as a result of several test runs for different parameters. A design parametric study was computed to walls in series from 4 to 16 m total height. The results showed different ratios of reinforcement per volume of concrete for three types of ground fill. Our main findings confirmed that the most sensitive variable for optimum walls is the wall-friction angle. The preference for wall-fill friction angles different to 0 in project design is confirmed. The type of fill is stated as the main key factor affecting the cost of optimum walls. The design parametric study shows that the soil foundation bearing capacity substantially affects costs, mainly in coarse granular fills (F1). In that sense, cost-optimum walls are less sensitive to the bearing capacity in mixed soils (F2) and fine soils of low plasticity (F3). Our results also showed that safety against sliding is a more influential factor for optimum buttressed walls than the overturning constraint. Finally, as for the results derived from the optimization procedure, a more suitable rule of thumb to dimension the footing thickness of the footing is proposed.This research was funded by the European Institute of Innovation and Technology under grant agreement no 20140262 Low Carbon Strategy in the Construction Industry (PGA_APED0094_2014-2.1-278-P066-10) and the Spanish Ministry of Economy and Competitiveness along with FEDER funding (Project BIA2014-56574-R).Molina Moreno, F.; García-Segura, T.; Martí Albiñana, JV.; Yepes, V. (2017). Optimization of buttressed earth-retaining walls using hybrid harmony search algorithms. Engineering Structures. 134:205-216. https://doi.org/10.1016/j.engstruct.2016.12.042S20521613

Generalised Adaptive Harmony Search: A Comparative Analysis of Modern Harmony Search

Harmony search (HS) was introduced in 2001 as a heuristic population-based optimisation algorithm. Since then HS has become a popular alternative to other heuristic algorithms like simulated annealing and particle swarm optimisation. However, some flaws, like the need for parameter tuning, were identified and have been a topic of study for much research over the last 10 years. Many variants of HS were developed to address some of these flaws, and most of them have made substantial improvements. In this paper we compare the performance of three recent HS variants: exploratory harmony search, self-adaptive harmony search, and dynamic local-best harmony search. We compare the accuracy of these algorithms, using a set of well-known optimisation benchmark functions that include both unimodal and multimodal problems. Observations from this comparison led us to design a novel hybrid that combines the best attributes of these modern variants into a single optimiser called generalised adaptive harmony search

Performance Analysis of Tree Seed Algorithm for Small Dimension Optimization Functions

Tree-Seed Algorithm (TSA) simulates the growth of trees and seeds on a land. TSA is a method proposed to solve continuous optimization problems. Trees and seeds indicate possible solutions in the search space for optimization problems. Trees are planted in the ground at the beginning of the search and each tree produces several seeds during iterations. While the trees were selected randomly during seed formation, the tournament selection method was used and also hybridized by adding the C parameter, which is the acceleration coefficient calculated according to the size of the problem. In this study, continuous optimization problem has been solved by the hybrid method. First, the performance analyses of the five best known numerical benchmark functions have been done, in both TSA and hybrid method TSA with 2, 3, 4 and 5 dimensions, and 10-50 population numbers. After that, well-known algorithms in the literature like Particle Swarm Optimization (PSO), TSA, Artificial Bee Colony (ABC), Harmony Search (HS), as well as hybrid method TSA (HTSA) have been applied to twenty-four numerical benchmark functions and the performance analyses of algorithms have been done. Hopeful and comparable conclusions based on solution quality and robustness can be obtained with the hybrid method