Multi-objective water cycle algorithm (MOWCA) is proposed for solving constrained and engineering multi-objective problems.Generational distance, metric of spread, and Δ metric are used as performance criteria.Optimal Pareto fronts are finely covered by the MOWCA with a good distribution of the non-dominated solutions.MOWCA is able to approach a full optimal Pareto front and provide a superior quality of solutions.MOWCA is better able to find a wider range of solutions compared with the other optimizers in this paper. In this paper, a metaheuristic optimizer, the multi-objective water cycle algorithm (MOWCA), is presented for solving constrained multi-objective problems. The MOWCA is based on emulation of the water cycle process in nature. In this study, a set of non-dominated solutions obtained by the proposed algorithm is kept in an archive to be used to display the exploratory capability of the MOWCA as compared to other efficient methods in the literature. Moreover, to make a comprehensive assessment about the robustness and efficiency of the proposed algorithm, the obtained optimization results are also compared with other widely used optimizers for constrained and engineering design problems. The comparisons are carried out using tabular, descriptive, and graphical presentations.

Water cycle algorithm for solving constrained multi-objective optimization problems

An evolutionary algorithm with directed weights for constrained multi-objective optimization

In order to solve the problem of global optimization, a new optimization method based on the law of universal gravitation is proposed, which is a random search algorithm derived from the simulation of natural imagination, which belongs to the category of evolutionary computation and is a new intelligent optimization algorithm. This method abstracts the global optimal value into a "black hole" in the universe, and other free celestial bodies in the universe approach the "black hole" by gravitational force, and are eventually "swallowed" by the black hole. The direction and step of the algorithm iteration are deduced through the formula of gravitation, which reduces the influence of the stochastic factors on the algorithm and improves the efficiency of the optimization calculation. Through numerical experiments, it is proved that the performance of the algorithm in global convergence and computing speed is superior to other optimization algorithms in the relevant literatures, and the specular reflection algorithm is also proven to be an effective and reliable optimization tool when solving high-dimensional optimization problems. It is applied to the optimization design of crank and rocker mechanism, and the calculation result further affirms the value of the algorithm in the field of engineering calculation. Keywords—global optimization; gravitation; numerical experiments; crank rocker

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Research on New Intelligent Optimization Algorithm Based on the Law of Universal Gravitation

Electric emergency robot can replace manual entry into power dangerous scene for emergency rescue operation. The rationality of robot path planning has a great impact on the timeliness and accuracy of its operation. A path planning method of emergency robot based on analytic hierarchy process, improved a * algorithm and gradient descent method is proposed. Firstly, the operation objectives in complex scenes are selected by analytic hierarchy process, and then the cost function of a * algorithm is improved by combining the hazard risk curve function and robot protection threshold to realize the global path planning focusing on risk, and then the local dynamic path planning in the movement process is completed based on the gradient descent method. Simulation results show that this method can independently select operation objectives, avoid operation risks and quickly reach the objectives, and effectively improve the timeliness and safety of power emergency operation.

Path Planning of Electric Emergency Robot based on Analytic Hierarchy Process

Bayesian multi-objective optimization of process design parameters in constrained settings with noise: an engineering design application

A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.

Constrained Multi-objective Particle Swarm Optimization Algorithm

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Constrained Multi-objective Particle Swarm Optimization Algorithm