scholarly journals Simulation Optimization Using Swarm Intelligence as Tool for Cooperation Strategy Design in 3D Predator-Prey Game

10.5772/5099 ◽  
2007 ◽  
Author(s):  
Emiliano G. ◽  
Marcos S. G. Tsuzuki
Keyword(s):  
Swarm Intelligence ◽  
Simulation Optimization ◽  
Predator Prey ◽  
Cooperation Strategy ◽  
Strategy Design

The Application of Swarm Intelligence to Collective Robots

2006 ◽  
pp. 157-185 ◽  
Author(s):  
Amanda J.C. Sharkey ◽  
Noel Sharkey
Keyword(s):  
Swarm Intelligence ◽  
Proof Of Concept ◽  
Practical Application ◽  
Predator Prey ◽  
Expected Benefits ◽  
Intelligent Approach ◽  
Intelligence Approach ◽  
Robotic Applications ◽  
Study Application

This chapter considers the application of swarm intelligence principles to collective robotics. Our aim is to identify the reasons for the growing interest in the intersection of these two areas, and to evaluate the progress that has been made to date. In the course of this chapter, we will discuss the implications of taking a swarm intelligent approach, and review recent research and applications. The area of “swarm robotics” offers considerable promise for practical application, although it is still in its infancy, and many of the tasks that have been achieved are better described as “proof-of-concept” examples, rather than full-blown applications. In the first part of the chapter, we will examine what taking a swarm intelligence approach to robotics implies, and outline its expected benefits. We shall then proceed to review recent swarm robotic applications, before concluding with a case study application of predator-prey robotics that illustrates some of the potential of the approach.

Parking Spaces Repurchase Strategy Design via Simulation Optimization

2015 ◽  
Vol 20 (3) ◽  
pp. 255-269 ◽  
Author(s):  
Wei Guo ◽  
Yi Zhang ◽  
Man Xu ◽  
Zuo Zhang ◽  
Li Li
Keyword(s):  
Simulation Optimization ◽  
Strategy Design

Simulation/Optimization Modeling for Robust Pumping Strategy Design

Ground Water ◽  
2006 ◽  
Vol 44 (4) ◽  
pp. 574-582 ◽  
Author(s):  
Ineke M. Kalwij ◽  
Richard C. Peralta
Keyword(s):  
Simulation Optimization ◽  
Optimization Modeling ◽  
Strategy Design

scholarly journals Swarm intelligence for groundwater management optimization

2010 ◽  
Vol 13 (3) ◽  
pp. 520-532 ◽  
Author(s):  
A. Sedki ◽  
D. Ouazar
Keyword(s):  
Swarm Intelligence ◽  
Groundwater Management ◽  
Simulation Optimization ◽  
Groundwater Resources ◽  
Flow Simulation ◽  
Optimization Techniques ◽  
Optimization Models ◽  
Simulation Code ◽  
Management Problems ◽  
Management Optimization

This paper presents some simulation–optimization models for groundwater resources management. These models couple two of the most successful global optimization techniques inspired by swarm intelligence, namely particle swarm optimization (PSO) and ant colony optimization (ACO), with one of the most commonly used groundwater flow simulation code, MODFLOW. The coupled simulation–optimization models are formulated and applied to three different groundwater management problems: (i) maximization of total pumping problem, (ii) minimization of total pumping to contain contaminated water within a capture zone and (iii) minimization of the pumping cost to satisfy the given demand for multiple management periods. The results of PSO- and ACO-based models are compared with those produced by other methods previously presented in the literature for the three case studies considered. It is found that PSO and ACO are promising methods for solving groundwater management problems, as is their ability to find optimal or near-optimal solutions.

scholarly journals Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

2021 ◽  
Vol 13 (24) ◽  
pp. 13551
Author(s):  
Mohamed Hussein ◽  
Abdelrahman E. E. Eltoukhy ◽  
Amos Darko ◽  
Amr Eltawil
Keyword(s):  
Swarm Intelligence ◽  
Construction Projects ◽  
Variance Reduction ◽  
Optimization Problems ◽  
Simulation Optimization ◽  
Computation Time ◽  
Bat Algorithm ◽  
Optimization Models ◽  
Planning Decisions ◽  
Whale Optimization

Off-site construction is a modern construction method that brings many sustainability merits to the built environment. However, the sub-optimal planning decisions (e.g., resource allocation, logistics and overtime planning decisions) of off-site construction projects can easily wipe away their sustainability merits. Therefore, simulation modelling—an efficient tool to consider the complexity and uncertainty of these projects—is integrated with metaheuristics, developing a simulation-optimization model to find the best possible planning decisions. Recent swarm intelligence metaheuristics have been used to solve various complex optimization problems. However, their potential for solving the simulation-optimization problems of construction projects has not been investigated. This research contributes by investigating the status-quo of simulation-optimization models in the construction field and comparing the performance of five recent swarm intelligence metaheuristics to solve the stochastic time–cost trade-off problem with the aid of parallel computing and a variance reduction technique to reduce the computation time. These five metaheuristics include the firefly algorithm, grey wolf optimization, the whale optimization algorithm, the salp swarm algorithm, and one improved version of the well-known bat algorithm. The literature analysis of the simulation-optimization models in the construction field shows that: (1) discrete-event simulation is the most-used simulation method in these models, (2) most studies applied genetic algorithms, and (3) very few studies used computation time reduction techniques, although the simulation-optimization models are computationally expensive. The five selected swarm intelligence metaheuristics were applied to a case study of a bridge deck construction project using the off-site construction method. The results further show that grey wolf optimization and the improved bat algorithm are superior to the firefly, whale optimization, and salp swarm algorithms in terms of the obtained solutions’ quality and convergence behaviour. Finally, the use of parallel computing and a variance reduction technique reduces the average computation time of the simulation-optimization models by about 87.0%. This study is a step towards the optimum planning of off-site construction projects in order to maintain their sustainability advantages.

The Application of Swarm Intelligence to Collective Robots

2011 ◽  
pp. 1444-1470
Author(s):  
Amanda J.C. Sharkey ◽  
Noel Sharkey
Keyword(s):  
Swarm Intelligence ◽  
Proof Of Concept ◽  
Practical Application ◽  
Predator Prey ◽  
Expected Benefits ◽  
Intelligent Approach ◽  
Intelligence Approach ◽  
Robotic Applications ◽  
Study Application

This chapter considers the application of swarm intelligence principles to collective robotics. Our aim is to identify the reasons for the growing interest in the intersection of these two areas, and to evaluate the progress that has been made to date. In the course of this chapter, we will discuss the implications of taking a swarm intelligent approach, and review recent research and applications. The area of “swarm robotics” offers considerable promise for practical application, although it is still in its infancy, and many of the tasks that have been achieved are better described as “proof-of-concept” examples, rather than full-blown applications. In the first part of the chapter, we will examine what taking a swarm intelligence approach to robotics implies, and outline its expected benefits. We shall then proceed to review recent swarm robotic applications, before concluding with a case study application of predator-prey robotics that illustrates some of the potential of the approach.

Cognition in predator-prey systems: Searching image and prey polymorphism

1997 ◽  
Author(s):  
Alan B. Bond ◽  
Alan C. Kamil ◽  
Christopher Cink
Keyword(s):  
Predator Prey
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