A novel and effective algorithm for numerical optimization: Melody Search (MS)

Ashrafi, Seyed Mohammad; Dariane, Alireza B.

doi:10.1109/his.2011.6122089

Cited by 32 publications

(14 citation statements)

References 7 publications

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“…Melody search (MeS) algorithm was originally proposed by Ashrafi and Dariane (2011). It is inspired by the basic concepts applied in HS, but unless the HS algorithm used a single HM, the MeS algorithm employed the procedure of the group improvisation [i.e., several memories called player memory (PM)] simultaneously for finding the best succession of pitches in a melody.…”

Section: Fundamentals Of Melody Search Algorithmmentioning

confidence: 99%

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Music Inspired Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, we introduce a set of music inspired algorithms, namely, harmony search (HS), melody search (MeS) algorithm, and method of musical composition (MMC) algorithm. We first describe the general knowledge of harmony in Sect. 14.1. Then, the fundamentals and performances of HS, MeS algorithm, and MMC algorithm are introduced in Sects. 14.2 and 14.3, respectively. Finally, Sect. 14.4 summarises this chapter.

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Section: Fundamentals Of Melody Search Algorithmmentioning

confidence: 99%

“…To evaluate the performance of MeS, five classical benchmark functions are tested in (Ashrafi and Dariane 2011). Compared with other CI methods [such as artificial bee colony (ABC), GA, HS, particle swarm optimization (PSO), and particle swarm and evolutionary algorithm (PS-EA)], the MeS is capable of finding better solutions.…”

Section: Performance Of Mesmentioning

confidence: 99%

Music Inspired Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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“…EHS dynamically updates pitch and bandwidth during the search process. On the other hand, in the SAHS algorithm, the pitch is dynamically updated but the bandwidth is completely remove [26]. These algorithms mimics performance processes of the musical improvisation for finding the best succession of pitches in a melody., utilizing different harmonic memories.…”

Section: State Of the Art In Hs Versions Focused On Parameters Improvements For The Last 10 Yearsmentioning

confidence: 99%

“…The eleven desired points to complete a perfect trajectory, can be observed in ( 22): The upper and lower limits of range for one of each design variables, are provided in s ( 23), ( 24), ( 25) and (26).…”

Section: B Case 2: Line As Path Generation Without Prescribed Timingmentioning

confidence: 99%

A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

et al. 2020

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There are problems that are difficult to solve through mathematical programming or by classical methods. These problems are called hard problems due to their high complexity or high dimension. On the other hand, mataheuristics intends to seek a better solution to a problem. The Improvement Harmony Search algorithm is proposed under modification of the bandwidth parameter increasing the quality of the exploitation of the solutions. That is why within the state of the art, are mentioned several versions of harmonic search. The state of the art is supports the fact that the algorithm belongs to the category of those who make modifications to its parameters. This research demonstrates the ability of ImHS to solve a problem of high complexity focused on solving four-bar mechanism designs, whose solutions imply high dimension and which are also classified as hard problems. The two problems that are solved in this investigation, are problems very attacked within the state of the art by various metaheuristics. A comparison is then made against previous solutions with traditional metaheuristics and other versions of harmony search algorithm. Finally, the effectiveness of performance is demonstrated, where proposed algorithm it exceeded five metaheuristic algorithms and five harmony search versions. An optimum is provided in an easy and useful way, the parametric statistics are improved and the number of feasible solutions is exceeded in NPhard problems as in the case of problems with four-bar mechanisms.INDEX TERMS algorithms performance, four-bar mechanism, improved harmony search, optimization problems, mechatronic.

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“…Given that human beings have higher survivability because they are good at observing and drawing experience from others' habits, the social cognitive optimization algorithm was proposed, with better solutions being selected by the imitating process and new solutions being produced by the observing process [12]. Imitating the toning process of musicians, the melody search is aimed at finding the best melody of continuity, the harmony memory considering rate controls the search range of each solution (harmony) and the pitch adjusting rate produces a local perturbation of the new solution [13]. The teaching-learning-based optimization algorithm is proposed through imitating the teachers' teaching and the students' learning process.…”

Section: Introductionmentioning

confidence: 99%

An Improved Squirrel Search Algorithm for Global Function Optimization

Wang

2019

Algorithms

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An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness.

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A novel and effective algorithm for numerical optimization: Melody Search (MS)

Cited by 32 publications

References 7 publications

Music Inspired Algorithms

Music Inspired Algorithms

A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

An Improved Squirrel Search Algorithm for Global Function Optimization

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