SOLVING 0 - 1 KNAPSACK PROBLEM BASED ON HYBRID GREEDY FIREWORKS
ALGORITHM

Aiming at the classical knapsack problem in combinatorial optimization,
in order to improve the local search ability and global search ability
of the basic fireworks algorithm, an improved fireworks algorithm is
proposed by combining the basic fireworks algorithm, greedy optimization
strategy and simulated annealing algorithm. In order to ensure the
diversity of the initial population, the Tent mapping is proposed to
initialize the population ; greedy repair operator and greedy
optimization operator are introduced to correct the intermediate
solution ; at the same time, the simulated annealing mechanism is
introduced to make the poor solution have a certain probability to be
accepted to improve the ability of the algorithm to jump out of the
local optimum. By solving the typical test function, it is found that
the improved fireworks algorithm can accurately solve the theoretical
optimal solution of the Griewank function. Compared with the basic
fireworks algorithm, simulated annealing algorithm and particle swarm
optimization algorithm, the improved fireworks algorithm can find the
optimal value of Sphere function with higher accuracy. By solving six
groups of knapsack problems with different dimensions, it is found that
the improved fireworks algorithm can hit the optimal solution with 100
% probability for most test data, and hit the optimal solution with
more than 70 % probability for its test data, and the standard
deviation of hit times is less than 13. The experimental results show
that the improved fireworks algorithm has higher solving accuracy and
faster solving speed, and can effectively solve the 0-1 knapsack
problem.