Biological Optimisation and Management
A section of Biomimetics (ISSN 2313-7673).
Section Information
Biologically inspired optimisation approaches use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. For example, so-called evolutionary algorithms perform well at approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape. Simple evolutionary algorithms can solve often complex problems. Another approach of biological optimisation is computer aided optimization that is based on the simulation of the growth rule of biological systems (tree, bone, etc.) and is therefore a method from the field of biomimetics. Theory- and simulation-based original publications as well as novel methodology papers are welcomed for this section.
Editorial Board
Topical Advisory Panel
Special Issues
Following special issues within this section are currently open for submissions:
- Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI (Deadline: 31 August 2025)
- Computational Biology Simulation, Agent-Based Modelling and AI (Deadline: 31 August 2025)
- Computer-Aided Biomimetics: 3rd Edition (Deadline: 31 August 2025)
- Biomimicry for Optimization, Control, and Automation: 3rd Edition (Deadline: 30 September 2025)
- Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition (Deadline: 31 October 2025)
- Biomimetic Fault Detection and System Health Management (Deadline: 30 November 2025)
- Advances in Brain–Computer Interfaces (BCI): Challenges and Opportunities (Deadline: 5 December 2025)
- Bioinspired Computational Intelligence and Optimization in Engineering Systems (Deadline: 10 December 2025)
- Exploration of Bio-Inspired Computing: 2nd Edition (Deadline: 20 December 2025)
- Advances in Biological and Bio-Inspired Algorithms (Deadline: 31 December 2025)
- Bio-Inspired AI: When Generative AI and Biomimicry Overlap (Deadline: 28 February 2026)
- Evolutionary and Nature-Inspired AI: Bridging the Gap Between Engineering and Computing (Deadline: 30 April 2026)