MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Future Generation Computer Systems
An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem
INFORMS Journal on Computing
D-Ants: savings based ants divide and conquer the vehicle routing problem
Computers and Operations Research
Computers and Operations Research
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Particle Swarm Optimization with Group Decision Making
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
Runtime analysis of an ant colony optimization algorithm for TSP instances
IEEE Transactions on Evolutionary Computation
A Multi-objective Gravitational Search Algorithm
CICSYN '10 Proceedings of the 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
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Despite insistent and breathtaking advances in computing, we continue to be humbled by the variety and adaptability of the natural world around us. Bio-inspired optimization is a term that covers a wide variety of computational approaches that are based on the principles of biological systems. This motivates the application of biology to optimization problems. Biologically inspired computing and optimization is a major subset of natural computation. This paper presents a critical survey of bio-inspired optimization techniques. There are many legacy optimization techniques available. This survey explains almost all important bio-inspired optimization techniques based on their development, intention, performance and application. It provides insight into determining the direction of future optimization techniques research.