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High-Level Modeling and Design Techniques
Published in Soumya Pandit, Chittaranjan Mandal, Amit Patra, Nano-Scale CMOS Analog Circuits, 2018
Soumya Pandit, Chittaranjan Mandal, Amit Patra
There are two important limitations of the traditional deterministic technique. First, it is often not possible to determine the gradient of the cost function. Second, the optimization process in many cases is quickly trapped in a local optimum of the cost function. Another problem is the rapid increase of the execution time with the increase in the number of design variables and design space. These techniques are used primarily for the fine tuning of sub-optimal sizings. On the other hand, in nontraditional stochastic techniques, the algorithm moves from one solution point to another with probabilistic transition rules, and the design variables are varied randomly. The derivatives of the cost function are not required. Greedy stochastic algorithms only accept a new set of variables if it reduces the cost function value. The main advantage of the stochastic methods over the deterministic ones is the capability to escape from local optimum and hence a higher probability to reach a global optimum. Because of this, these algorithms are very popular to solve engineering design problems. Examples include simulated annealing, genetic algorithms, particle swarm optimization algorithms, ant colony optimization algorithms, etc.
Setting the Stage: Complex Systems, Emergence and Evolution
Published in Mariam Kiran, X-Machines for Agent-Based Modeling, 2017
Falling under area of swarm intelligence, ant colonies are extremely efficient in finding shortest possible routes to food in minimum time. Proposed in Dorigo’s PhD work [53], ant colony optimization algorithms can solve complex problems like the travelling salesmen problem and network routing problems for dynamic scenarios (Figure 1.6).
Environmental friendly route design for a milk collection problem: the case of an Indian dairy
Published in International Journal of Production Research, 2022
Jabir E., Vinay V. Panicker, R. Sridharan
The evolutionary meta-heuristic algorithms are considered intelligent and quick solution providers within the artificial intelligence community. The bio-inspired meta-heuristics can learn and adapt to providing feasible solutions to very complex problems. This domain of intelligent meta-heuristics has been applied to aid decision making in a wide area of engineering applications (Kar 2016). Meta-heuristics hybridised with local search has been proved to be effective and quick to find good quality solutions on VRP variants (Koç, Laporte, and Tükenmez 2020). As the ant algorithms mimic the foraging behaviour of a colony of ants, they are classified under a distinct category of evolutionary algorithms named as swarm intelligence (Mullen et al. 2009). Generally, swarm intelligence attributes to intelligent agents inspired by nature. The Ant Colony Optimization (ACO) algorithm employs ants as multi-agents that communicate through the secretion of chemical particles named as pheromones. The Ant algorithms were popularised in the late nineties. The basic model of the ant system (Dorigo, Maniezzo, and Colorni 1996) was later modified into a more general framework of Ant Colony Optimization algorithms (Dorigo and Gambardella 1997). Dorigo and Di Caro (1999) and Dorigo, Di Caro, and Gambardella (1999) present the formalised version of the ACO algorithm. The rank-based Ant Systems (Bullnheimer, Hartl, and Strauss 1999) and MAX–MIN Ant System (Stützle and Hoos 2000) were the later editions to the famed ACO algorithms.
A unified pedestrian routing model for graph-based wayfinding built on cognitive principles
Published in Transportmetrica A: Transport Science, 2018
Peter M. Kielar, Daniel H. Biedermann, Angelika Kneidl, André Borrmann
Herding behavior, a common phenomenon in pedestrian dynamics, has been discussed in a number of research publications. Some models include social group behavior, queuing, or group cohesiveness, which can be understood as herding behavior (Pan et al. 2007; Seitz, Köster, and Pfaffinger 2014; von Sivers et al. 2014; Kneidl 2015). However, the herding behavior we address in this paper focuses on herding emerging during wayfinding. A number of methods to describe herding during wayfinding have been proposed already. For example, Schadschneider, Kirchner, and Nishinari (2003) as well as Kneidl and Borrmann (2011) describe methods that are based on the concept of the ‘Ant Colony Optimization’ algorithms. Another approach is the concept of fastest paths, where pedestrians attempt to find promising paths by following other pedestrians who walk fast (Kneidl and Borrmann 2011). Generally, herding in wayfinding has been modeled before. Nonetheless, an integrative framework including the herding aspects with spatial-cognitive theories has yet not been proposed.