We address a scheduling problem on CNC machines. In this problem, a human operator sets some workpieces and their designated jigs on pallets and installs these pallets on the CNC machine in order. The entire schedule of both CNC machines and human operators is modeled into a class of job shop scheduling problems and represented as a mixed integer programming model. We design a new optimization method that hybridizes metaheuristics and mathematical programming to obtain near-optimal solutions at a reasonable computational cost. We examine the potential of our method through some numerical experiments using real-world data from machine tool manufacturers.

Surrogate-assisted PSOs are one of the most popular black-box optimizers for computationally expensive optimization problems, but those employ approximation models less scalable for the increase of the problem dimension on a restricted number of fitness evaluations. This paper proposes a hybrid surrogate-assisted PSO (HyPSO), which utilizes approximation and classification surrogate models for computationally expensive optimization problems. A basic idea of HyPSO is in the hybridization of surrogate model types, although existing works only consider the approximation model compatible with the PSO framework. HyPSO intends to manage a trade-off between approximation/classification models in terms of the model accuracy and the screening capacity; this contributes to hedge the risk of the over-fitting issue in building surrogates under a restriction of fitness evaluations. In particular, HyPSO constructs an approximation model with Radial Basis Function (RBF) and a classification model with Support Vector Machine (SVM). Then, it estimates a global best solution and a personal-best solution of a particle with an RBF model and an SVM model, respectively. Experimental results show that HyPSO significantly outperforms an alternative approach, i.e., OPUS and the standard PSO on a set of single-objective benchmark functions. Especially, HyPSO has a good scalability against the increase of the problem dimension.

In Japan today, the demand for operational efficiency has increased due to the declining birthrate and aging population. However, many efficiency tools to solve these problems require a special IT environment. Therefore, in this research, we developed an application which automatically creates work shift tables for restaurants using general-purpose tools such as VBA and GAS. Furthermore, we examined the genetic algorithm for optimizing shift scheduling. Our application was developed for a work shift table in a weak for a restaurant in which approximately 30 part-time and full-time workers are involved. We used an evaluation function considering each point for each worker. Our genetic algorithm was designed to maximize the sum of the points of applicable workers, while the dispersion of the sum in the weak would be small. The outputs of our genetic algorithm were compared with the real work shift tables used in the restaurant. The holiday correct rate in the work shift table, which is one of important parameters for the operation of the real restaurant, was found to be as high as 80% in the outputs of our application. The calculation time in our genetic algorithm was less than 1 minute using a common laptop computer. Our application has been successfully utilized in the real restaurant, and favorable comments have been received from the users.

This paper proposes the Misclassification Detection and Correction method based on Conditional variational autoencoder (MDC/C) which detects and corrects the incorrect output of Learning Classifier System (LCS) through a comparison between the original data and the restored data by Conditional Variational Auto-Encoder (CVAE) with the output of LCS (as the condition to CVAE).The experimental results on the complex multi-class classification problem of the handwritten numerals have revealed the following implications: (1) although an integration of XCSR (i.e., the real-valued LCS) with CVAE (called CVAEXCSR) increases the correct rate in comparison with XCSR, it has the limit of improvement, i.e., the correct rate converges to 87.92%; (2) the correct rate of CVAEXCSR increases to 99.04% when removing the incorrect outputs by the detection mechanism of MDC/C and 95.03% when correcting them by the correction mechanism of MDC/C, respectively; and (3) the correct rate of CVAEXCSR with MDC/C is high from the first iterations and keeps it high even after the rule condensation which executes LCS without the crossover and mutation operations.

The Evolutionary Computation Competition (EC-Comp) is an optimization competition launched in 2017 to promote real-world applications of evolutionary computation and interaction between industry and academia. For 2017—2019, the competition has focused on continuous optimization problems in the manufacturing and aerospace industries. With the aim of exploring new areas of applications, EC-Comp2020 focused on "Designing Random Numbers to Entertain Game Players" in the game industry. Random numbers used in video games are usually generated by general-purpose pseudo random number generators, such as Mersenne Twister and Xorshift. However, these mathematically unbiased random numbers often make game players feel biased (sometimes even deliberately chosen), causing strong frustration. It is known that humans have various biases toward probabilistic events, and unbiased random numbers seem rather biased to game players. This competition asked to design a random number sequence that makes game players feel unbiased (but actually biased). This paper describes the definition of the random number design problem for entertaining game players in EC-Comp2020. This paper also explains the participants' optimization methods, accompanied with brief analysis on their results.

This paper focuses on the multi-swarm optimization for real robots to find the multiple local optimal solutions by multiple swarms, and proposes Niching Migratory Multi-Swarm Optimiser without Generating and Deleting Solutions (NMMSO-WoGDS) by extending Niching Migratory Multi-Swarm Optimiser (NMMSO) for the real robot environments. NMMSO can find multiple solutions with the small number of evaluations, but it generates and deletes the solutions which are infeasible in the real robot environment. To overcome this problem, NMMSO-WoGDS is improved from NMMSO from the following viewpoints: (1) the fix number of the individuals (corresponding to the robots); (2) the movement of the individuals instead of generating/deleting them; (3) the simultaneous process of the individual location update instead of its sequential process; and (4) the limited range of the movement of the individuals instead of the unlimited range of the movement. The experiment of the testbed functions has revealed the following implications: (1) NMMSO-WoGDS can find all multiple local optimal solutions with a smaller number of the individuals than NMMSO, and NMMSO-WoGDS can find them with a smaller number of the iterations than NMMSO in the case of the same number of individuals; (2) the iterations of NMMSO-WoGDS in the simultaneous process is smaller than those of NMMSO-WoGDS in the sequential process; (3) the iterations of NMMSO-WoGDS are not drastically affected by the limited range of movement of individuals.

In this study, a multi-fidelity approach was developed based on the efficient global optimization (EGO) and integrated with multi-additional sampling. The developed approach was more efficient than the conventional multi-fidelity approach when applied to design problems. The effectiveness of the proposed approach was demonstrated by solving two test problems (a test problem in Van Valedhuizen’s test suite and a test problem with a convex Pareto front) before applying the approach to real-world problems. As a demonstration of solving real-world problem, we solved two objective airfoil design problems for a small unmanned airplane. The objective functions were the drag coefficient (for flight efficiency) and the thickness at the 75% chord position (for structural strength and manufacturability). The results of the test problems revealed that the proposed approach obtained more non-dominant solutions near the theoretical Pareto front than those obtained by the Original optimization approach at the same iteration number of EGO loop; this is because the proposed approach obtained more additional samples than the Original optimization approach (multi-objective multi-fidelity EGO without multi-additional sampling) per additional sampling loop. A comparison of the accuracies of surrogate models based on the proposed approach and the Original optimization approach using leave-one-out cross validation suggested that, depending on the optimization problem, one of the two approaches can yield greater accuracy. The airfoil design results, as well as the test problems, revealed that the proposed approach can obtain several better solutions than those obtained by the Original optimization approach when the number of iterations of additional sampling was the same between both approaches. The hypervolume in the proposed approach also increases more rapidly than that in the Original optimization approach.