Improving artificial algae algorithm for solution of constrained optimization problems

Optimization is the job of finding the best solution under certain conditions. Many of the optimization problems that exist in the real world have constraints. Constraints allocate the search space as applicable and non-applicable areas. The most challenging part of such problems is the processing of constraints. The original of many existing metaheuristic optimization algorithms is designed for unconstrained problems. The methods of handling constraints are methods that are added to guide the search in areas where these algorithms are appropriate. Artificial Algae Algorithm (AAA) is a metaheuristic optimization algorithm which is inspired by the life behaviors of micro algae. The AAA has demonstrated its success in unconstrained problems, but there are no versions of it to solve constraints problems. Tests on engineering design optimization problems were performed to observe the effect of Ap parameter and population number change on AAA. AAAdr, AAAdp and AAAϵ algorithms, which can solve the constrained optimization problems by applying Deb's Rule, dynamic penalty and ϵ-constraint handling technique on AAA, have been proposed. The performance of the proposed algorithms has been tested in a restricted set of functions. AAAdr, AAAdp and AAAϵ were compared and the prominent AAAdr was compared with the algorithms adapted for other well-known limited problems in the literature. The studies revealed, AAAdr seem to produce competitive results.