An improved simulated annealing algorithm based on residual network for permutation flow shop scheduling

Abstract

The permutation flow shop scheduling problem (PFSP), which is one of the most important scheduling types, is widespread in the modern industries. With the increase of scheduling scale, the difficulty and computation time of solving the problem will increase exponentially. Adding the knowledge to intelligent algorithms is a good way to solve the complex and difficult scheduling problems in reasonable time. To deal with the complex PFSPs, this paper proposes an improved simulated annealing (SA) algorithm based on residual network (SARes). First, this paper defines the neighborhood of the PFSP and divides its key blocks. Second, the Residual Network (ResNet) is used to extract and train the features of key blocks. And, the trained parameters are stored in the SA algorithm to improve its performance. Afterwards, some key operators, including the initial temperature setting and temperature attenuation function of SA algorithm, are also modified. After every new solution is generated, the parameters trained by the ResNet are used for fast ergodic search until the local optimal solution found in the current neighborhood. Finally, the most famous benchmarks including part of TA benchmark are selected to verify the performance of the proposed SARes algorithm, and the comparisons with the-state-of-art methods are also conducted. The experimental results show that the proposed method has achieved good results by comparing with other algorithms. This paper also conducts experiments on network structure design, algorithm parameter selection, CPU time and other problems, and verifies the advantages of SARes algorithm from the aspects of stability and efficiency.

A novel ameliorated Harris hawk optimizer for solving complex engineering optimization problems

“…For both the cases studied and verified in the current work, the proposed AHHO algorithm as well as the other algorithms used for comparison are implemented to determine the optimal control parameter settings in VCRPD problem. Figures 4–12 provides illustration of results for IEEE 57 bus system. The convergence curve for Case 1 with first objective of transmission loss reduction and second objective of operating cost reduction is depicted in Figures 4 and 5, respectively.…”

“…where D is the dimension of search space, S is the randomly selected vector of dimension 1 × D. LF (D) is the levy flight function. 37 So, the position of hawks is updated in this case by following Equation ( 9) where Z and A are obtained from Equations ( 7) and (8), respectively.…”

“…They are designed and implemented for handling numerous large‐scale, multimodal real‐world problems owing to their simplicity and ease of implementation. Meta‐heuristic algorithms are classified into the following presiding classes: evolutionary which is conceptualized on Darwin's survival of fittest theory like genetic algorithm (GA), 3,4 swarm intelligence techniques which capitalizes on collective swarm intelligence like particle swarm optimization (PSO), 5,6 behavior of bioinspired algorithms like salp swarm algorithm 7 and algorithms based on physical processes like simulated annealing 8 and gravitational search algorithm (GSA) 9 . Some metaheuristic techniques can also be human‐based like teaching learning‐based optimization (TLBO) 10 and volleyball premier league algorithm 11 .…”

Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

“…Some stochastic-searchbased algorithms such as meta-heuristic-based or even hybrid meta-heuristic-based approaches typically require sufficient sampling of candidate solutions in the search space and have shown robust performance on a variety of scheduling problems. In this regards, genetic algorithms (GAs) [8, 31-33, 64, 65] particle swarm optimization (PSO) [40,43], Hybrid discrete PSO (HDPSO) [1], ant colony optimization (ACO) [47,66], artificial bee colony algorithm (ABC) [52, 67] simulated annealing (SA) [35,36,68] cuckoo search algorithm (CS) [52, 93], the memetic discrete differential evolution algorithm [69] and tabu search (TS) [64,70] have been successfully applied to different scheduling problems. Among them, GAs have been widely utilized to evolve solutions for many task scheduling problems [8, 31-33, 64, 65].…”

Optimization of product line considering compatibility and reliabilityviadiscrete imperialist competitive algorithm

“…When exploring solution space, SA will accept an inferior solution probabilistically according to the metropolis criterion, jump out of the local optima, and obtain the global optimal solution [58]. It has been widely used in various optimization problems due to its strong local searching ability, easy operation and fast solving speed [59]. SA for the proposed model is described in Algorithm 6.…”