Abstract
A novel metaheuristic algorithm for global optimization, called the Solar System Algorithm (SSA), is presented. The proposed algorithm imitates the orbiting behavior of some objects found in the solar system: i.e., Sun, planets, moons, stars, and black holes. SSA is used to solve five well-known engineering design problems: three-bar truss, pressure vessel, tension/compression spring, welded beam, and gear train. The obtained results are compared to 16 state-of-the-art metaheuristic algorithms. They show that SSA is very competitive in solving the considered engineering problems. In addition, the performance of SSA is evaluated on the benchmarks CEC 2014 and CEC 2020. The experimental results are compared to 27 (12 for CEC 2014 and 15 for CEC 2020) metaheuristic algorithms. They demonstrate that SSA is very promising in finding efficient solutions.
Highlights
With the technologies’ progress, the complexity of engineering industries has greatly increased in the last few decades
Small values of inclination and orbiting angles (i.e., θ pi, θ mj, θ pi, and θ mj, ), lead the Solar System Algorithm rather to exploit the search space: i.e., planets and moons move with small steps
The problem consists of the minimization of a nonlinear fitness function, subject to: 4 discrete decision variables since each gear must have a positive integer of teeth
Summary
With the technologies’ progress, the complexity of engineering industries has greatly increased in the last few decades. Derivative-free: all metaheuristic algorithms do not require derivatives to solve the considered optimization problems This is very beneficial, especially when the search space is very complex and contains multiple local minima. Many initial solutions are used and improved over generations In the latter, some information on the search space is assumed to be shared between the population’s agents. Each metaheuristic algorithm introduces its parameters to find a proper balance between exploration and exploitation Such parameters are empirically set for a specific problem (values of parameters are not known beforehand). A novel swarm-based metaheuristic algorithm for global optimization, called the Solar System Algorithm (SSA), is introduced in this paper It mimics the orbiting behaviors of the Sun, planets, moons, stars, and black holes in the galaxy.
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