Research Article
A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization
Algorithm 3
The hybrid meta-heuristic algorithm of HS/BA.
Begin | Step 1: Initialization. Set the generation counter ; initialize the population of NP | bats randomly and each bat corresponding to a potential | solution to the given problem; define loudness ; set frequency , | the initial velocities , and pulse rate ; set the harmony memory consideration | rate HMCR, the pitch adjustment rate PAR and bandwidth bw; | set maximum of elite individuals retained KEEP. | Step 2: Evaluate the quality for each bat in determined by the objective function . | Step 3: While the termination criteria is not satisfied or < MaxGeneration do | Sort the population of bats from best to worst by order of quality for each bat. | Store the KEEP best bats as KEEPBAT. | for :NP (all bats) do | | | if (rand > ) then | | end if | for : (all elements) do //Mutate | if then | | where | if (rand < PAR) then | | endif | else | | endif | endfor | Evaluate the fitness for the offsprings , , | Select the offspring with the best fitness among the offsprings , , . | if (rand < ) then | ; | end if | Replace the KEEP worst bats with the KEEP best bats KEEPBAT stored. | end for | ; | Step 4: end while | Step 5: Post-processing the results and visualization; | End. |
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