scholarly journals Evolutionary Game Theory in Multi-Objective Optimization Problem

2010 ◽  
Vol 3 (sup01) ◽  
pp. 74-87 ◽  
Author(s):  
Maozhu Jin ◽  
Xia Lei ◽  
Jian Du
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Optimization Problem ◽  
Evolutionary Game ◽  
Multi Objective Optimization ◽  
Multi Objective

A Swarm Intelligence Method Combined to Evolutionary Game Theory Applied to the Resources Allocation Problem

2012 ◽  
Vol 3 (2) ◽  
pp. 20-38 ◽  
Author(s):  
Cédric Leboucher ◽  
Rachid Chelouah ◽  
Patrick Siarry ◽  
Stéphane Le Ménec
Keyword(s):  
Game Theory ◽  
Swarm Intelligence ◽  
Evolutionary Game Theory ◽  
Optimization Problem ◽  
Evolutionary Game ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Multi Objective Optimization ◽  
Discrete Particle Swarm Optimization ◽  
Large Populations

This paper addresses an allocation problem and proposes a solution using a swarm intelligence method. The application of swarm intelligence has to be discrete. This allocation problem can be modelled as a multi-objective optimization problem where the authors minimize the time and the distance of the total travel in a logistic context. This study uses a hybrid Discrete Particle Swarm Optimization (DPSO) method combined to Evolutionary Game Theory (EGT). One of the main implementation issues of DPSO is the choice of inertial, individual, and social coefficients. In order to resolve this problem, those coefficients are optimised by using a dynamical approach based on EGT. The strategies are either to keep going with only inertia, only with individual, or only with social coefficients. Since the optimal strategy is usually a mixture of the three, the fitness of the swarm can be maximized when an optimal rate for each coefficient is obtained. Evolutionary game theory studies the behaviour of large populations of agents who repeatedly engage in strategic interactions. Changes in behaviour in these populations are driven by natural selection via differences in birth and death rates. To test this algorithm, the authors create a problem whose solution is already known. This study checks whether this adapted DPSO method succeeds in providing an optimal solution for general allocation problems.

scholarly journals SuSy-EnGaD: Surveillance System Enhanced by Games of Drones

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Daniel H. Stolfi ◽  
Matthias R. Brust ◽  
Grégoire Danoy ◽  
Pascal Bouvry
Keyword(s):  
Game Theory ◽  
Case Studies ◽  
Evolutionary Game Theory ◽  
Surveillance System ◽  
Evolutionary Game ◽  
Area Coverage ◽  
Multi Objective ◽  
The Third ◽  
Pareto Fronts ◽  
Single Objective

In this article, we propose SuSy-EnGaD, a surveillance system enhanced by games of drones. We propose three different approaches to optimise a swarm of UAVs for improving intruder detection, two of them featuring a multi-objective optimisation approach, while the third approach relates to the evolutionary game theory where three different strategies based on games are proposed. We test our system on four different case studies, analyse the results presented as Pareto fronts in terms of flying time and area coverage, and compare them with the single-objective optimisation results from games. Finally, an analysis of the UAVs trajectories is performed to help understand the results achieved.

scholarly journals A Study of Taxi Service Mode Choice Based on Evolutionary Game Theory

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Zhu Bai ◽  
Mingxia Huang ◽  
Shuai Bian ◽  
Huandong Wu
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Mode Choice ◽  
Evolutionary Game ◽  
Payoff Matrix ◽  
Game Model ◽  
Stable States ◽  
Taxi Service ◽  
Service Mode ◽  
The Impact

The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management.

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