Harmony search algorithm
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Company LOGO Scientific Research Group in Egypt (SRGE) Harmony search algorithm Dr. Ahmed Fouad Ali Suez Canal University, Dept. of Computer Science, Faculty of Computers and informatics Member of the Scientific Research Group in Egypt .
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Harmony search algorithm
Transcript of Harmony search algorithm
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LOGO
Scientific Research Group in Egypt (SRGE)
Harmony search algorithm
Dr. Ahmed Fouad AliSuez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt .
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LOGO Outline
1.Harmony search algorithm (History and main idea)1.Harmony search algorithm (History and main idea)
3. Improvisation of new harmony vectors3. Improvisation of new harmony vectors
7. References 7. References
2. Initialization of harmony memory2. Initialization of harmony memory
6. Application of the harmony search Algorithm 6. Application of the harmony search Algorithm
4. Harmony memory updating4. Harmony memory updating
5. Harmony search algorithm5. Harmony search algorithm
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LOGO Harmony search algorithm (History and main idea)
• Harmony search (HS) is a population based metaheuristics algorithm inspired from the musical process of searching for a perfect state of harmony.
• HS has been proposed by Geem et al. in (2001)
• The pitch of each musical instrument determines the aesthetic quality, just as the fitness function value determines the quality of the decision variables.
• In the music improvisation process, all
players sound pitches within possible range together to make one harmony.
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LOGO Harmony search algorithm (History and main idea)
• If all pitches make a good harmony, each player stores in his memory that experience and the possibility of making a good harmony
is increased next time.
• The same thing in optimization, the initial solution is generated randomly from decision variables within the possible range.
• If the objective function values of these decision variables is good to make a promising
solution, then the possibility to make a good solution is increased next time.
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LOGOInitialization of harmony memory
• The initial population HM contains of HMS vectors is generated randomly, where
xi = xij , i = 1, …,HMS and j = 1, …, n.
• The HM matrix is filled with HMS vectors as follows:
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LOGOImprovisation of new harmony vectors
Harmony memory considering (HMC) rule.
• For this rule, a new random number r1 is generated within the range [0,1].
• If r1 < HMCR, where HMCR is the harmony memory consideration rate, then the first decision variable in the new vector xij
{new} is chosen randomly from the values in the current HM as follows:
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LOGOImprovisation of new harmony vectors (cont)
Pitch adjusting rate (PAR).
• The obtained decision variables from the harmony memory consideration rule is further examined to determined if it needs to pitch adjustment or not.
• A new random number r2 is generated within the range [0 1].
• If r2 < PAR, where PAR is a pitch adjustment rate, then the pitch adjustment decision variable is calculated as follows:
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LOGOImprovisation of new harmony vectors (cont)
Pitch adjusting rate (PAR).
where BW is a bandwidth factor, which is used to control the local search around the selected decision variable in the new vector.
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LOGOImprovisation of new harmony vectors (cont)
Random initialization rule
If the condition r1 < HMCR fails, the new first decision variable in the new vector x ij
{new} is generated randomly as follows:
where l, u is the lower and upper bound for the given problem.
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LOGO Harmony memory updating
After the harmony vector x{new} is generated, it will replace the worst harmony vector x{worst} in the harmony memory if its objective function value is better than the objective function value of the worst harmony vector.
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LOGO Harmony search algorithm
Memory consideration step
Pitch adjustment
Random initialization
Harmony memory update
Parameter setting Initial population
(harmony memory
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LOGO Harmony search Flowchart
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LOGO
• Engineering optimization problems
• NP hard combinatorial optimization problems
• Data fusion in wireless sensor networks
• Nanoelectronic technology based operation-amplifier• (OP-AMP)
• Train neural network
• Manufacturing scheduling
• Nurse scheduling problem
Application of the HS Algorithm
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LOGO References
• Z.W. Geem, J.-H. Kim, G.V. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation 76 (2) (2001) 60–68.
• K.S. Lee, Z.W. Geem, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice, Comput. Methods Appl. Mech. Engrg. 194 (2005) 3902–3933
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