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Advances in Engineering Software is a peer-reviewed journal published by Elsevier, covering Human auditory perception and evaluation, Numerical methods in engineering, Topology Optimization in Engineering. Over its lifetime it has published 4,284 papers, which have received 143,252 citations.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Advances in Engineering Software 12 (2023) 45–58.
Formats any DOI in Advances in Engineering Software style. No sign-up. |
| Publishes research in | Human auditory perception and evaluation Numerical methods in engineering Topology Optimization in Engineering Diverse Scientific and Economic Studies Advanced Multi-Objective Optimization Algorithms |
| ISSN | 0965-9978 |
| Citation impact (2-yr) | 6.25 |
| h-index | 119 |
| i10-index | 2,110 |
| Article processing charge | $4,190 |
| Top institutions publishing here | Czech Technical University in Prague |
| Journal website | www.journals.elsevier.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three…
This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization…
This paper proposes an optimisation algorithm called Grasshopper Optimisation Algorithm (GOA) and applies it to challenging problems in structural optimisation. The proposed algorithm mathematically models and mimics the behaviour of grasshopper swarms in nature for solving optimisation problems. The GOA algorithm is first benchmarked on a set of test problems including CEC2005 to test and…
