The main objective of this paper is to present a hybrid technique named as a PSO-GA for solving the constrained optimization problems. In this algorithm, particle swarm optimization (PSO) operates in the direction of improving the vector while the genetic algorithm (GA) has been used for modifying the decision vectors using genetic operators. The balance between the exploration and exploitation abilities have been further improved by incorporating the genetic operators, namely, crossover and mutation in PSO algorithm. The constraints defined in the problem are handled with the help of the parameter-free penalty function. The experimental results of constrained optimization problems are reported and compared with the typical approaches exist in the literature. As shown, the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature. Furthermore, experimental results indicate that the proposed approach may yield better solutions to engineering problems than those obtained by using current algorithms.
The main goal of the present paper is to solve structural engineering design optimization problems with nonlinear resource constraints. Real world problems in engineering domain are generally large scale or nonlinear or constrained optimization problems. Since heuristic methods are powerful than the traditional numerical methods, as they don't requires the derivatives of the functions and provides near to the global solution. Hence, in this article, a penalty guided artificial bee colony (ABC) algorithm is presented to search the optimal solution of the problem in the feasible region of the entire search space. Numerical results of the structural design optimization problems are reported and compared. As shown, the solutions by the proposed approach are all supe- rior to those best solutions by typical approaches in the literature. Also we can say, our results indicate that the proposed approach may yield better solutions to engineering problems than those obtained using current algorithms.
In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO). The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks in nature called surprise pounce. In this intelligent strategy, several hawks cooperatively pounce a prey from different directions in an attempt to surprise it. Harris hawks can reveal a variety of chasing patterns based on the dynamic nature of scenarios and escaping patterns of the prey. This work mathematically mimics such dynamic patterns and behaviors to develop an optimization algorithm. The effectiveness of the proposed HHO optimizer is checked, through a comparison with other nature-inspired techniques, on 29 benchmark problems and several real-world engineering problems. The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques. Source codes of HHO are publicly available at http://www.alimirjalili.com/HHO.html and http://www.evo-ml.com/2019/03/02/hho.
This paper introduces an improved accelerated particle swarm optimization algorithm (IAPSO) to solve constrained nonlinear optimization problems with various types of design variables. The main improvements of the original algorithm are the incorporation of the individual particles memories, in order to increase swarm diversity, and the introduction of two selected functions to control balance between exploration and exploitation, during search process. These modifications are used to update particles positions of the swarm. Performance of the proposed algorithm is illustrated through six benchmark mechanical engineering design optimization problems. Comparison of obtained computation results with those of several recent meta-heuristic algorithms shows the superiority of the IAPSO in terms of accuracy and convergence speed.
Differential evolution (DE) is a powerful evolutionary algorithm (EA) for numerical optimization. Combining with the constraint-handling techniques, recently, DE has been successfully used for the constrained optimization problems (COPs). In this paper, we propose the adaptive ranking mutation operator (ARMOR) for DE when solving the COPs. The ARMOR is expected to make DE converge faster and achieve feasible solutions faster. In ARMOR, the solutions are adaptively ranked according to the situation of the current population. More specifically, the population is classified into three situations, i.e., infeasible situation, semi-feasible situation, and feasible situation. In the infeasible situation, the solutions are ranked only based on their constraint violations; in the semi-feasible situation, they are ranked according to the transformed fitness; while in the feasible situation, the objective function value is used to assign ranks to different solutions. In addition, the selection probability of each solution is calculated differently in different situations. The ARMOR is simple, and it can be easily combined with most of constrained DE (CDE) variants. As illustrations, we integrate our approach into three representative CDE variants to evaluate its performance. The 24 benchmark functions presented in CEC 2006 and 18 benchmark functions presented in CEC 2010 are chosen as the test suite. Experimental results verify our expectation that the ARMOR is able to accelerate the original CDE variants in the majority of test cases. Additionally, ARMOR-based CDE is able to provide highly competitive results compared with other state-of-the-art EAs.