The permutation flow shop scheduling problem (PFSSP) is well-applied in the industry, which is confirmed to be an NP-Hard optimization problem, and the objective is to find the minimum completion time (makespan). A modified coronavirus herd immunity optimizer (CHIO) with a modified solution update is suggested in this work. Meanwhile, the simulated annealing strategy is used on the updating herd immunity population to prevent trapping on local optima, and an adjusted state mechanism is involved to prevent fast state change/ convergence. Nine instances of different problem scales on the FPSSP dataset of Taillard were tested. The experimental results show that the proposed method can find the optimal solutions for the tested instances, with ARPDs no more than 0.1, indicating that the proposed method can effectively and stably solve the PFSSP.

The data preprocess directly affects the classification results in various applications. In the field of intrusion detection, less research raised the problems or solutions of unequal metrics in data attributes. This study proposes an effective data preprocessing method for network packets with unequal metrics in packet attributes. A standard deviation standardization was first applied to standardize each attribute of KDDCUP'99 dataset, followed by quantizing it to the range of 0 to 255 interval for afterward use of the image. Meanwhile, the Zigzag arrangement coding and IDCT (Inverse Discrete Cosine Transform) were then used to convert the quantized data into images. Experimental results demonstrate that a more than 94% recall rate of the overall intrusion detection classifier can be yielded by the proposed preprocess method even without a complicated network model. Meanwhile, intrusion detection performance can be guaranteed by using small-size images of packet attributes.

The vehicle routing problem with simultaneous pickups and deliveries (VRPSPD) considers the delivery and pickup demands problem in the vehicle routing problem (VRP), so the customers allocated to the vehicle should not exceed the vehicle carrying capacity during the visit. The VRPSPD is regarded as solving two sub-problems in the work, customer bases determination and best routes decision. Hence, this paper proposes a two-layer discrete particle swarm optimization (DPSO) for solving the VRPSPD. The outer layer DPSO is used to find the optimal allocation of vehicles of customers (customer bases) to meet the demands for delivery and pickup in the route of the visit. The inner DPSO is used to obtain the optimal routes of various vehicles. Meanwhile, the roulette wheel selection is applied as the mechanism for gaining the discrete particle positions. Finally, the VRPSPD of CMT1X type in OR Library is tested. The experimental results demonstrate that the method designed in this study is able to solve the vehicle routing problem with simultaneous pickups and deliveries effectively.

In this paper an unsupervised parallel approach called fuzzy competitive learning network (FCLN) for vector quantization (VQ) and spread FCLN (SFCLN) for color image compression in the mean value/difference value transform (MDT) domain are proposed. In the FCLN, the codebook design is conceptually considered as a clustering problem. Here, it is a kind of competitive learning network model imposed by the fuzzy clustering strategies working toward minimizing an objective function defined as the average distortion measure between any two training vectors within the same class. The color image information transformed by the MDT operation was separated into RGB 3-plane mean value and detail coefficients. Then the detail coefficients for each plane were trained using the proposed SFCLN method to generate the VQ codebook. The experimental results show that promising codebooks can be obtained using the proposed FCLN and SFCLN for color image compression in the MDT domain.

The Hopfield neural network is extensively applied to obtaining an optimal/feasible solution in many different applications such as the traveling salesman problem (TSP), a typical discrete combinatorial problem. Although providing rapid convergence to the solution, TSP frequently converges to a local minimum. Stochastic simulated annealing is a highly effective means of obtaining an optimal solution capable of preventing the local minimum. This important feature is embedded into a Hopfield neural network to derive a new technique, i.e., mean field annealing. This work applies the Hopfield neural network and the normalized mean field annealing technique, respectively, to resolve a multiprocessor problem (known to be a NP-hard problem) with no process migration, constrained times (execution time and deadline) and limited resources. Simulation results demonstrate that the derived energy function works effectively for this class of problems.

The traveling salesman problem (TSP), a typical combinatorial explosion problem, has been well studied in the AI area, and neural network applications to solve the problem are widely surveyed as well. The Hopfield neural network is commonly referred to in finding an optimal solution and a fast convergence to the result, however, it often traps to a local minimum. Stochastic simulated annealing has an advantage in finding the optimal solution; it provides a chance to escape from the local minimum. Both significant characteristics of the Hopfield neural network structure and stochastic simulated annealing algorithm are combined together to yield a so called mean field annealing technique. A complicated job scheduling problem of a multiprocessor with multiprocess instance under execution time limitation process migration inhibited and bounded available resource constraints is presented. An energy based equation is developed first whose structure depends on precise constraints and acceptable solutions using an extended 3D Hopfield neural network (HNN) and the normalized mean field annealing (MFA) technique; a variant of mean field annealing was conducted as well. A modified cooling procedure to accelerate a reaching equilibrium for normalized mean field annealing was applied to the study. The simulation results show that the derived energy function worked effectively, and good and valid solutions for sophisticated scheduling instance can be obtained using both schemes.

This paper presents an unsupervised segmentation approach applying the mean field annealing (MFA) heuristic with the modified cost function. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentation is chosen as the minimization of the Euclidean distance between samples to cluster centers. To resolve the optimal problem using a Hopfield or simulated annealing neural network, the penalty terms are combined into a weighted sum using several coefficients determined by user. Using the MFA network to medical image segmentation, the need for finding weighting factors in the energy function can be eliminated and the rate of convergence is much faster than that of simulated annealing. The experimental results show that good and valid solutions can be obtained using the MFA neural network.