Evolutionary algorithm for bin packing problem

More documents

Recommendations

Info

in GGA; there is also the penalty term as the element of the fitness function.3. Evolutionary based heuristicThe version of the evolutionary strategy used by the author is the result of his studies of evolutionary algorithms inflowshop problem [7]. No attempts were made to fine-tune any of the algorithm parameters. That is (1, λ) - ESwherein λ descendants are generated from one parent using simple operators. No crossover is applied. The best ofthe descendants becomes the new parent solution.The overall procedureThe general form of the applied algorithm written in PASCAL pseudo-codes is presented below.BEGINgenerate and evaluate random starting solution;best solution:= start solution;REPEATFOR i:=1 to λ DOBEGINcopy parent to create child number i ;mutate child i;END;evaluate children;choose best child based on objective function to be a new parent;UNTIL (max. generation);output best solution;END.Chromosome representationPermitted solutions are represented by the list of n elements and s group separators, whereas the value j (1≤j≤n)can appear only once and so can the value i (n+1≤i≤n+s), indicating the separator number.Therefore, for 7 elements and 3 group separators the solution in the form R 1= (1,2,9,8,5,2,7,10,6,4) willmean that the elements form three bins (1,3); (5,2,7); (6,4), while the solution R 2=(1,10,3,8,5,2,9,7,6,4) meansthat the elements ought to be packed into four bins (1), (3), (5,2), (7,6,4).It is accepted that s=ROUND (0.7* n)The initial populationThe initial population was obtained in a random manner.The fitness functionIn the algorithm the maximum of the following function was sought:FD =N∑i = 1Fis⋅ ⎛ ⎝ ⎜ ⎞⎟C ⎠N2whereN - the number of bins in the given solutionFi - the sum of weights of the elements packed into the bin i, i=1..NC - the bin capacitys- a penalty constant, such that:⎧ 1s = ⎨⎩−1for F ≤ Cifor F > Ci2
Applied operatorsThe algorithm made use of the operators presented in the publications on the subject, that is: exchange andinsertion mutations and inversion. The characteristic feature of the algorithm, the one distinguishing it from thoseproposed by other authors is that both elements and separators are subjected to genetic operators.The final version of the evolution strategy for the BPP is characterised by the elements and parameterssummarised in Table 1.Fitness functionPopulationRepresentationReproductionInitializationSelection for the nextgenerationTermination criterionCrossoverOther operators4. Experimental testsTable 1. Anatomy of the evolution strategy for the bin packing problemFD =N∑i = 1Fis⋅ ⎛ ⎝ ⎜ ⎞⎟C ⎠N2a list (sequence) of n parts and s separators of the groups1 parent, 10 descendants1 randomly generated solutionthe best descendant replaces the parentafter realization of 45 000 generationsnot appliedexchange, insertion and inversion applied at an equal probabilityTests were run using 8 sets with 20 problems each, the problems being included in the Beasley’s library [2]. Theseproblems were divided into two groups:• elements whose weights were drawn at random from the uniform distribution of the interval [20...100] are tobe placed in the bins of the size 150, the number of elements being 120, 250, 500, 1000,• elements whose values are taken from the interval [0.25...0.50] are to be placed in the bins of the size 1, thenumber of elements being 60, 120, 249, 501- 1/3 of which has considerable weight, while the weigh of theremaining 2/3 is rather small (less than 1/3 of the bin size).It must be emphasised that ES itself involves some random searching. Therefore, each calculation using the sameset of input data may yield different solution. Because of that, three runs were completed for each set of data, thenthe best solution was selected. During one run the algorithm would analyse 450 000 solutions, which is a minorpart of the search space. It is comparable to the value applied by Khuri et al. (1000 generations, each yielding 500solutions).Table 2 presents the results obtained for well-known approximate algorithms for 2 out of 8 sets from theBeasley’s library: binpack4 for 1000 elements and binpack8 for 501 elements, which are the major and the mostdifficult problems from the first and the second group of problems, respectively.Results of those tests have confirmed the superiority of Falkenauer’s GGA algorithm, especially in relation toproblems involving large dimensions. Algorithm proposed by Martello and Toth and FFD are adequate whilesolving the problems from the first group. In the case of those considered by Falkenauer to be more difficult, thesealgorithms prove inadequate. The algorithm by Khuri et al. gives the worst results. The evolution strategydeveloped by the author yields slightly less satisfactory results than GGA proposed by Falkenauer. It is moreadvantageous that other techniques, especially in relation to the second group of problems.3
Page 4: Table 2. Runs performed by FFD, GGA

Evolutionary algorithm for bin packing problem

Create successful ePaper yourself

Delete template?

Save as template?