An engineering design optimization problem can be formulated as a search of the solution space for which the relationships among various groups of attributes are given. The search can be also conducted for a multicriteria optimization. The paper discusses three classes of problems associated with multicriteria optimization in engineering design. First, multicriteria optimization methods with a single domination relationship are reviewed. Next, such methods with many domination functions are discussed. The last part of the paper focuses on the search when the ideal solution, or solutions, are known, but they are infeasible and the solutions sought should come as close as possible.

Multicriteria Optimization in Engineering Design

This paper presents an improved teaching-learning-based optimization (TLBO) algorithm for solving optimization problems, called RLTLBO. First, a new learning mode considering the effect of the teacher is presented. Second, the Q-Learning method in reinforcement learning (RL) is introduced to build a switching mechanism between two different learning modes in the learner phase. Finally, ROBL is adopted after both the teacher and learner phases to improve the local optima avoidance ability of RLTLBO. These two strategies effectively enhance the convergence speed and accuracy of the proposed algorithm. RLTLBO is analyzed on 23 standard benchmark functions and eight CEC2017 test functions to verify the optimization performance. The results reveal that proposed algorithm provides effective and efficient performance in solving benchmark test functions. Moreover, RLTLBO is also applied to solve eight industrial engineering design problems. Compared with the basic TLBO and seven state-of-the-art algorithms, the results illustrate that RLTLBO has superior performance and promising prospects for dealing with real-world optimization problems. The source codes of the RLTLBO are publicly available at https://github.com/WangShuang92/RLTLBO.

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An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems

• A novel multi-objective sparrow search algorithm (MOSSA) is proposed. • 2 k crowding-distance entropy is proposed to maintain the diversity. • The formulas of updating positions and adaptive parameters are modified. • The optimal strategy of the positions is added to MOSSA. This study proposes a multi-objective sparrow search algorithm (MOSSA) based on a 2 k crowding-distance entropy and the optimal strategy of the positions to solve complex multi-objective optimisation problems (MOPs) using the excellent performance of the sparrow search algorithm (SSA). A fast non-dominated sorting approach is incorporated to permit SSA to solve MOPs. To maintain the evenness and spread of the Pareto solution set obtained in each run, a 2 k crowding-distance entropy is proposed to measure the diversity of the solution set. Modified formulas for updating positions and additional adaptive parameters can improve the global search ability of MOSSA. The position archive of the population is introduced to realise the optimal strategy of the positions. This strategy ensures that the positions of the population in each generation are optimal, which significantly improves the performance of MOSSA. MOSSA is compared with three well-known algorithms using a set of complex unconstrained test problems and a complex constrained engineering optimisation problem. This study explores the effect of the 2 k crowding-distance entropy and crowding distance on maintaining diversity. The impact of the k in 2 k crowding-distance entropy on the performance of MOSSA is analysed. The experimental results demonstrate that MOSSA exhibits competitive performance in solving complex MOPs. 

Multi-objective sparrow search algorithm: A novel algorithm for solving complex multi-objective optimisation problems

The problems of a 7-degree of freedom (DOF) manipulator with rapid and continuous response to uncertain fast-flying objects are addressed: 1) how to effectively solve trajectory planning of the 7-DOF manipulator with multiple criteria; and 2) how to make the 7-DOF manipulator realize the rapid and continuous response to uncertain fast-flying objects. In the proposed approach, based on the trajectory parameterization of the 7-DOF manipulator, a multiobjective teaching-learning-based optimization (MOTLBO) algorithm is adopted to find a close representation of the Pareto optimal set rather than a single solution. As such, an optimal solution can be chosen as digital knowledge information. A new methodology based on a knowledge base representing and learning the operation environment, that is, skill digitization, is presented, which enables the 7-DOF manipulator to realize the rapid and continuous response skill. Simulation and practical testing results of a ping-pong robot validate the feasibility and effectiveness of the proposed approach, in which the online trajectory generation spends only around 1 ms.

Knowledge Database-Based Multiobjective Trajectory Planning of 7-DOF Manipulator With Rapid and Continuous Response to Uncertain Fast-Flying Objects

COVID-19 has impacted Indonesia and caused an economic recession during 2020. The economic condition in Indonesia should be evaluated through the regional economic condition. One well-known approach to do a regional analysis is a geodemographic analysis using Fuzzy Geographically Weighted Clustering (FGWC). However, FGWC is still weak against the local optima, so it is necessary to use an optimisation algorithm to enhance it. This study proposes a new approach of FGWC enhancement using Elicit Teaching-Learning Based Optimisation (ETLBO) to analyse the regional economic condition in Indonesia. We compare ETLBO with previously implemented optimisation algorithms in FGWC, such as Particle Swarm Optimisation (PSO) and Intelligent Firefly Algorithm (IFA). This study found that ETLBO performs well in identifying Indonesia’s regional economic condition. Moreover, the clustering results showed the difference of problematic sectors. We also found that the provinces in Java Island joined into a cluster and have problems in many sectors. This study can be used as the basis for the evaluation of regional economic conditions in Indonesia.

Regional Economy Condition in Indonesia during COVID-19 Pandemic: An Analysis using Teaching Learning-Based Fuzzy Geodemographic Clustering

Students generally attempt to improve themselves with different manners in their spare time. Inspired by this fact, this paper presents a multi-objective elitist feedback teaching–learning-based optimization (MEFTO) algorithm for multi-objective optimization problems. To promote the exploration capacity and convergence of the basic teaching–learning-based optimization (TLBO), a feedback phase that simulates the spare-time learning phenomenon is introduced at the end of the learner phase. Students should compare their score with the average class score to select an appropriate means for further improvement. Poorly performing students can learn from the teacher directly for rapid improvement, whereas high-performing students prefer to motivate themselves for reinforcement learning. Non-dominated sorting is incorporated to permit this heuristic to solve problems with several objectives. Crowding distance calculation is adopted to maintain the diversity of the obtained solution set in a single run. Elitism strategy is also employed to provide a great improvement for the algorithm. The performance of the MEFTO algorithm is compared with seven other well-known algorithms by using a set of unconstrained benchmark test problems and three constrained engineering optimization problems. The qualitative and quantitative results indicate that the proposed algorithm can provide considerably competitive results and outperforms the other algorithms.

A multi-objective elitist feedback teaching–learning-based optimization algorithm and its application

The embodiment of the invention discloses a method for simultaneously positioning and mapping in the multi-glass partition environment. According to the method, a plurality of position data are fusedto form basic data, then the basic data are used for scanning and mapping, and loopback optimization is carried out on a newly-built map, so the glass is effectively identified, the built map is accurate and comprehensive, and implementation of autonomous navigation functions such as subsequent path planning of the mobile robot is facilitated.

Jiang Ruiqing

Papers

A multi-objective elitist feedback teaching–learning-based optimization algorithm and its application

Method for simultaneously positioning and mapping in multi-glass partition environment