Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms for handling many-objective (having four or more objectives) optimization problems. In this paper, we recognize a few recent efforts and discuss a number of viable directions for developing a potential EMO algorithm for solving many-objective optimization problems. Thereafter, we suggest a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NSGA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points. The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm (MOEA/D). While each of the two MOEA/D methods works well on different classes of problems, the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper. This paper presents results on unconstrained problems, and the sequel paper considers constrained and other specialties in handling many-objective optimization problems.

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An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints

This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html .

SCA: A Sine Cosine Algorithm for solving optimization problems

Salp Swarm Algorithm

Partly due to lack of test problems, the impact of the Pareto set (PS) shapes on the performance of evolutionary algorithms has not yet attracted much attention. This paper introduces a general class of continuous multiobjective optimization test instances with arbitrary prescribed PS shapes, which could be used for studying the ability of multiobjective evolutionary algorithms for dealing with complicated PS shapes. It also proposes a new version of MOEA/D based on differential evolution (DE), i.e., MOEA/D-DE, and compares the proposed algorithm with NSGA-II with the same reproduction operators on the test instances introduced in this paper. The experimental results indicate that MOEA/D could significantly outperform NSGA-II on these test instances. It suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated PS shapes.

Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II

English courses are a compulsory course for non-English core courses. Studying English at university is an important part of studying at a non-English university. The purpose of a university education in English is to enhance the comprehensive language skills of students in English. In this way, they can communicate effectively in English during future work, study, and social interactions, while strengthening their ability to meet the needs of social development and international exchange. The functional improvement design of the college English network teaching system will help the system to provide technical support and services for college English teaching more effectively. It can more effectively cultivate students’ language communicative ability and autonomous learning ability. This article first improves the practical units of the education system and then adds the decision tree algorithm. It uses improved decision-making algorithms to evaluate student performance with an improved learning system. The number of leaf nodes of the improved decision tree algorithm on the three datasets is 19, 14, and 15, respectively, and the number of summary points is 27, 23, and 7, respectively.

Improvement Design of Functional Modules of College English Network Teaching System Based on Decision Tree Algorithm

The invention discloses a phase modulator and a resonant cavity heterogeneous integrated chip. A silicon nitride modulation resonance module is arranged on a lithium niobate thin film substrate; the silicon nitride modulation resonance module comprises a lithium niobate waveguide phase modulation unit, an interlayer coupling unit and a silicon nitride resonance unit; the input end of the lithium niobate waveguide phase modulation unit receives light output by a laser, and the lithium niobate waveguide phase modulation unit modulates the input light to obtain modulated light; the interlayer coupling unit realizes transition of modulated light from the lithium niobate waveguide phase modulation unit to the silicon nitride resonance unit; the modulated light is coupled to the silicon nitrideresonance unit through the interlayer coupling unit; the silicon nitride resonance unit resonates the coupled light, and then the output end of the silicon nitride resonance unit outputs the resonatedlight to a detector. By adopting the chip provided by the invention, the lithium niobate phase modulator and a resonant cavity are integrated on one chip, so that the size can be reduced, the power consumption and the cost can be reduced, and the stability of a resonant integrated optical gyro system can be improved.

Phase modulator and resonant cavity heterogeneous integrated chip

Autism in children is a representative disease of pervasive developmental disorder, and there is currently no matching treatment. In the course of treatment, people generally use special education training, but this training mode cannot meet the comprehensive development of children. The traditional treatment mode cannot meet the changing new situation due to its poor adaptability. Martial arts routine movements are the soul of martial arts training, which can lay the foundation for movement training. Based on this, this article takes children with autism as research objects and aims to analyze the comprehensive intervention effect of martial arts routines on autism so as to provide some references for the treatment of children with autism. The article first analyzes the intervention forms of martial arts routine movements and realizes the combination of martial arts routine movements and children's autism intervention treatment from a theoretical point of view. Then, the article randomly divides the experimental subjects into the experimental group and the control group and conducts an evaluation and analysis of various indicators before and after the training of children with autism. Finally, the article falls back from the experimental results to related theories, aiming to provide a scientific basis for the comprehensive intervention treatment of children with autism. The experimental results showed that the scale evaluation results of the experimental group are different from those of the control group, and various indicators have been improved and improved, including language, perception, and social skills. Among them, the language and behavior of children with autism increased by 19.1%. The perception ability increased by 20.7%, and the social ability increased by 5.9%. The above experiments and analysis fully demonstrated that martial arts routines have a good effect in the intervention and treatment of children with autism, which can well improve the comprehensive quality of patients.

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Comprehensive Intervention and Effect of Martial Arts Routines on Children with Autism

The invention discloses a tunnel and underground engineering full cross-section cantilever tunneling machine milling and excavating device and method. The device comprises a machine body. A walking portion is arranged at the bottom of the machine body. The machine body and the walking portion are connected through a rotating mechanism. The lower portion of the front end of the machine body is connected with a shoveling plate through a first air cylinder. The upper end of the front end of the machine body is connected with a cutting portion through a second air cylinder. The front end of the cutting portion is provided with a milling and excavating head. The milling and excavating head is of a hemispherical structure. The two sides of the cutting portion are provided with infrared distancers. An electrical control system is arranged on the machine body. The device is simple in structural principle, capable of effectively limiting compressional deformation and converging deformation of an excavated rock body on an excavating face in front of the excavating face, advanced supporting is not performed on the condition that the surrounding rock level is not poor, the tunnel excavating cost is saved, and the excavating efficiency of the cantilever tunneling machine is improved.

Hui Li

Papers

Improvement Design of Functional Modules of College English Network Teaching System Based on Decision Tree Algorithm

Phase modulator and resonant cavity heterogeneous integrated chip

Comprehensive Intervention and Effect of Martial Arts Routines on Children with Autism

Tunnel and underground engineering full cross-section cantilever tunneling machine milling and excavating device and method