Search

The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO is essentially an unconstrained algorithm and requires constraint handling techniques (CHTs) to solve constrained optimization problems (COPs). For this purpose, we integrate two CHTs, the superiority of feasibility (SF) and the violation constraint-handling (VCH), with a PSO. These CHTs distinguish feasible solutions from infeasible ones. Moreover, in SF, the selection of infeasible solutions is based on their degree of constraint violations, whereas in VCH, the number of constraint violations by an infeasible solution is of more importance. Therefore, a PSO is adapted for constrained optimization, yielding two constrained variants, denoted SF-PSO and VCH-PSO. Both SF-PSO and VCH-PSO are evaluated with respect to five engineering problems: the Himmelblau's nonlinear optimization, thewelded beam design, the spring design, the pressure vessel design, and the three-bar truss design. The simulation results show that both algorithms are consistent in terms of their solutions to these problems, including their different available versions. Comparison of the SF-PSO and the VCHPSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations used. We also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants. [ABSTRACT FROM AUTHOR]

This paper proposes a memetic algorithm by integrating adaptively a local search approach with a recently proposed variant of differential evolution, reflected adaptive differential evolution with two external archives (RJADE-TA). The main objective is to bring together the exploration factor of differential evolution and exploitative component of local search to solve continuous optimization problems. A novel hybrid local search mechanism is proposed and demonstrated leading to a crossbred version of RJADE-TA. In other words, the best solutions after a regular toll of global search are migrated to an archive, where Davidon Fletcher Powell local search method is implemented to the migrated solutions. Afterwards, the population is updated with new reflected solutions to prevent premature convergence. The proposed approach is novel in the sense that most of the algorithms store only inferior or superior solutions in the archives. None of the algorithms implemented the local search inside the archive. Thus, this combination is a new one. To evaluate the merit of developed meme, a benchmark suite of complex 28 functions from CEC 2013 test problems is selected and implemented. The experimental results demonstrate that this integration of local search strategy can further improve the performance of RJADE-TA. They further reveal that the proposed meme outperforms differential evolution based algorithms on most of the tested problems. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon–Fletcher–Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the archive and refined there by DFP. Thus, both kinds of searches, global and local, are incorporated alternatively. Furthermore, a population minimization approach is also proposed. At each call of DFP, the population is decreased. The algorithm starts with a maximum population and ends up with a minimum. The proposed technique was tested on a test suite of 28 complex functions selected from literature to evaluate its merit. The results achieved demonstrate that DE complemented with LS can further enhance the performance of RJADE/TA. [ABSTRACT FROM AUTHOR]