A novel optimization method for solving constrained and unconstrained problems: modified Golden Sine Algorithm

Abstract

Recently, the metaheuristic optimization algorithms inspired by nature and different science branches have been powerful solution methods for unconstrained, constrained, and engineering problems. Various metaheuristic optimization algorithms have been proposed and they have been applied to problems in different fields. This paper proposes a novel optimization method based on a modified version of the Golden Sine Algorithm for solving unconstrained, constrained, and engineering problems. The basic idea behind the proposed modified Golden Sine Algorithm (GoldSA-II) depends on finding the optimum solution field in search space by using the decreasing pattern of the sine function and the golden ratio. The performance of the proposed GoldSA-II is evaluated using 19 unconstrained benchmark functions, five constrained optimization test problems, and five real engineering design problems. The results of the proposed GoldSA-II are compared with best-known optimization algorithms using some well-known criteria. The obtained results show that the GoldSA-II converges more accurately to the global solution in many benchmark functions.

A New Chaotic S-Box Generation Method Using Parameter Optimization of One Dimensional Chaotic Maps

“…Standard test functions are generally used for performance evaluation of optimization algorithms [1], [3], [14]. These test functions allow the optimization algorithms to be evaluated in different ways.…”

“…This entropy source has been used in the design of the substitutional boxes (s-boxes), which is a basic cryptographic primitive. In the study, the optimal initial conditions and control parameters have been determined for four different discrete time chaotic systems using seven different common known optimization algorithms Differential Evolution (DE) [7], [8], Particle Swarm Optimization (PSO) [9], [10], Symbiosis Organisms Search (SOS) Algorithm [11], Gravitational Search Algorithm (GSA) [12], Harmony Search Algorithm (HS) [13], Golden Sine Algorithm II (GoldSA-II) [14]. Chi-square test has been used as the goal function of optimization algorithms.…”

Comparison of metaheuristic optimization algorithms with a new modified deb feasibility constraint handling technique

“…If a solution that does not satisfy at least one constraint, that is, the solution is in the infeasible region, then a very high value is assigned due to the constraint violation. Therefore, the objective function created can be expressed as in Equation (3).…”

“…The main purpose of the solution process in optimization problems is to minimize or maximize the performance, duration, efficiency, and productivity parameters. Most of the optimization problems in real-world contain some constraints defined on decision variables [2][3][4].…”

Dynamic software rejuvenation in web services: a whale optimizationalgorithm-based approach

“…Metaheuristic algorithms are widely used to find the global optimum of real engineering problems. The disadvantages of existing numerical methods concerning factors such as simplicity, efficiency, and accuracy encourage researchers to rely on metaheuristic algorithms based on methods that are inspired by nature or different branches of science to solve engineering optimization problems [33,34]. Some of the most prominent nature-inspired metaheuristic algorithms are particle swarm optimization (PSO) [35], the butterfly optimization algorithm (ALO) [36], genetic algorithms (GAs) [37], and the whale optimization algorithm (WOA) [38].…”