A survey on traffic optimization problem using biologically inspired techniques
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A survey on traffic optimization problem using biologically inspired techniques Sweta Srivastava1
•
Sudip Kumar Sahana2
Ó Springer Nature B.V. 2019
Abstract Nature is a great source of inspirations for solving complex computational problems. The inspirations can come from any source like some theory of physics or chemistry, a mathematical concept or from the biological world. Several biologically inspired techniques are implemented in various areas of research and development. These technologies can be grouped into two broad segments: Evolutionary and Swarm based depending on the nature of inspiration. This paper presents an overview of these biologically inspired techniques and its various implementations for traffic optimization with an objective to optimize congestion, minimize wait time, improve safety and reduce pollution. Keywords Biological inspiration Genetic algorithm Genetic programming Ant colony optimization Differential evolution Particle swarm optimization Artificial bee colony Traffic optimization Network design problem
1 Introduction Nature had always been a great source of inspiration for painters, poets, philosophers, researchers and many more. Right from nano-sized chromosomes, antibodies, microorganisms, to complex structures of organisms (like a human being, animals), the environment they live, diversity among them, ability to adapt to the ever-changing environment, interaction with similar organism and everchanging environment, food foraging behaviour, self organization had always been mysterious as well as the great source of inspiration for researchers. There are a number of difficulties associated with large-scale engineering problems. Several techniques that are mimicking of these biological environments are developed to solve these complex computational and optimization problems. Biologically inspired techniques, however, does not promise to
& Sweta Srivastava [email protected] Sudip Kumar Sahana [email protected] 1
Department of CSE, ASET, Amity University, Noida, India
2
Department of CSE, BIT Mesra, Ranchi, India
give an exact solution, but near optimum solution can be reached. The biologically inspired techniques can be grouped into two categories- Evolutionary and Swarm algorithms. Evolutionary algorithms are stochastic search methods that mimic the metaphor of natural biological evaluation (Elbeltagia et al. 2004). The family comprises technologies like genetic algorithms (GA), genetic programming (GP), differential evolution (DE). cellular automata (CA) (Kamalika Bhattacharjee et al. 2018) is another important evolutionary technique which is inspired by biological self reproduction. Swarm-based algorithms are inspired from social behaviour of various species like the way ants find the shortest path to its food source, birds find their destination during migration or honey bees selects some sites and ignore others (Stutzle 2004) Self-interaction and organization is a prime source of inspiration.
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