In order to adapt the ABC algorithm for solving constrained optimization problems,
we adopted Deb’s constrained handling method [13] instead of the selection
process (greedy selection) of the ABC algorithm described in the previous
section since Deb’s method consists of very simple three heuristic rules. Deb’s
method uses a tournament selection operator, where two solutions are compared
at a time, and the following criteria are always enforced: 1) Any feasible solution
is preferred to any infeasible solution, 2) Among two feasible solutions, the
one having better objective function value is preferred, 3) Among two infeasible
solutions, the one having smaller constraint violation is preferred.
Because initialization with feasible solutions is very time consuming process
and in some cases it is impossible to produce a feasible solution randomly, the
ABC algorithm does not consider the initial population to be feasible. Structure
of the algorithm already directs the solutions to feasible region in running process
due to the Deb’s rules employed instead of greedy selection. Scout production
process of the algorithm provides a diversity mechanism that allows new and
probably infeasible individuals to be in the population.
In order to produce a candidate food position from the old one in memory,
the adapted ABC algorithm uses the following expression:
In order to adapt the ABC algorithm for solving constrained optimization problems,
we adopted Deb’s constrained handling method [13] instead of the selection
process (greedy selection) of the ABC algorithm described in the previous
section since Deb’s method consists of very simple three heuristic rules. Deb’s
method uses a tournament selection operator, where two solutions are compared
at a time, and the following criteria are always enforced: 1) Any feasible solution
is preferred to any infeasible solution, 2) Among two feasible solutions, the
one having better objective function value is preferred, 3) Among two infeasible
solutions, the one having smaller constraint violation is preferred.
Because initialization with feasible solutions is very time consuming process
and in some cases it is impossible to produce a feasible solution randomly, the
ABC algorithm does not consider the initial population to be feasible. Structure
of the algorithm already directs the solutions to feasible region in running process
due to the Deb’s rules employed instead of greedy selection. Scout production
process of the algorithm provides a diversity mechanism that allows new and
probably infeasible individuals to be in the population.
In order to produce a candidate food position from the old one in memory,
the adapted ABC algorithm uses the following expression:
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