A comprehensive review of swarm optimization algorithms

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches

Framework for Smart Online 3D Bin Packing Using Augmented Reality

Given the growth of the e-commerce market and the increasing demands, it is crucial to come up with an efficient and optimized way to pack products. Furthermore, the advent of new technologies and the fourth industrial revolution opened up a range of research areas and opportunities to expand the scope of classic packing applications. In this context, this work presents a framework to assist operators with an immersive application to assure smart packing. For that purpose, several heuristics that take into consideration multiple conditions imposed by the nature of the product are embedded to solve the online 3D Bin Packing Problem. Then the best packing solution is sent to the Augmented Reality developed application to immerse the operator in the packing process. This framework is designed for industries that rely on manual packing and aims to automate the process and/or provide intelligent decision-aid tools to ensure a smart packing process

An improved nature inspired meta-heuristic algorithm for 1-D bin packing problems

Bin packing problem (BPP) is a classical combinatorial optimization problem widely used in a wide range of fields. The main aim of this paper is to propose a new variant of whale optimization algorithm named improved Lévy-based whale optimization algorithm (ILWOA). The proposed ILWOA adapts it to search the combinatorial search space of BPP problems. The performance of ILWOA is evaluated through two experiments on benchmarks with varying difficulty and BPP case studies. The experimental results confirm the prosperity of the proposed algorithm in proficiency to find the optimal solution and convergence speed. Further, the obtained results are discussed and analyzed according to the problem size.No Full Tex