A Multi-agent Optimization Approach to Determine Farmers’ Market Locations in Bogotá City, Colombia

2021 ◽  
pp. 343-355
Author(s):  
Daniela Granados-Rivera ◽  
Gonzalo Mejía
Keyword(s):  
Optimization Approach ◽  
Farmers Market ◽  
Multi Agent

scholarly journals A Multi-Agent Proposal for the Resolution of BIBD instances

2016 ◽  
Vol 39 (2) ◽  
pp. 267 ◽  
Author(s):  
David Rodriguez ◽  
Enrique Darghan ◽  
Julio Monroy
Keyword(s):  
Combinatorial Optimization ◽  
Experimental Design ◽  
Tabu Search ◽  
Local Search ◽  
Current Literature ◽  
Hill Climbing ◽  
Optimization Approach ◽  
Search Spaces ◽  
Multi Agent ◽  
Multi Agents

<p>The problem with designing balanced incomplete blocks (BIBD) is enclosed within the combinatorial optimization approach that has been extensively used in experimental design. The present proposal addresses thi problem by using local search techniques known as Hill Climbing, Tabu Search, and an approach based considerable sized the use of Multi-Agents, which allows the exploration of diverse areas of search spaces. Furthermore, the use of a vector vision for the consideration associated with vicinity is presented. The experimental results prove the advantage of this technique compared to other proposals that are reported in the current literature.</p>

scholarly journals Minimizing Convergence Error in Multi-Agent Systems Via Leader Selection: A Supermodular Optimization Approach

2014 ◽  
Vol 59 (6) ◽  
pp. 1480-1494 ◽  
Author(s):  
Andrew Clark ◽  
Basel Alomair ◽  
Linda Bushnell ◽  
Radha Poovendran
Keyword(s):  
Optimization Approach ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Convergence Error ◽  
Multi Agent ◽  
Leader Selection

scholarly journals Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2867 ◽  
Author(s):  
Zhanle Wang ◽  
Raman Paranjape ◽  
Zhikun Chen ◽  
Kai Zeng
Keyword(s):  
Power System ◽  
Demand Response ◽  
Electricity Market ◽  
Energy Cost ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
System Stability ◽  
Optimization Approach ◽  
Model Case ◽  
Multi Agent

Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.

scholarly journals Aggregation–Decomposition-Based Multi-Agent Reinforcement Learning for Multi-Reservoir Operations Optimization

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2688 ◽  
Author(s):  
Milad Hooshyar ◽  
S. Jamshid Mousavi ◽  
Masoud Mahootchi ◽  
Kumaraswamy Ponnambalam
Keyword(s):  
Reinforcement Learning ◽  
Stochastic Optimization ◽  
Optimization Problems ◽  
Transition Probability ◽  
Optimization Under Uncertainty ◽  
Stochastic Dynamic ◽  
Optimization Approach ◽  
Reservoir Operations ◽  
Multi Agent ◽  
Operations Optimization

Stochastic dynamic programming (SDP) is a widely-used method for reservoir operations optimization under uncertainty but suffers from the dual curses of dimensionality and modeling. Reinforcement learning (RL), a simulation-based stochastic optimization approach, can nullify the curse of modeling that arises from the need for calculating a very large transition probability matrix. RL mitigates the curse of the dimensionality problem, but cannot solve it completely as it remains computationally intensive in complex multi-reservoir systems. This paper presents a multi-agent RL approach combined with an aggregation/decomposition (AD-RL) method for reducing the curse of dimensionality in multi-reservoir operation optimization problems. In this model, each reservoir is individually managed by a specific operator (agent) while co-operating with other agents systematically on finding a near-optimal operating policy for the whole system. Each agent makes a decision (release) based on its current state and the feedback it receives from the states of all upstream and downstream reservoirs. The method, along with an efficient artificial neural network-based robust procedure for the task of tuning Q-learning parameters, has been applied to a real-world five-reservoir problem, i.e., the Parambikulam–Aliyar Project (PAP) in India. We demonstrate that the proposed AD-RL approach helps to derive operating policies that are better than or comparable with the policies obtained by other stochastic optimization methods with less computational burden.

Aircraft Emergency Evacuation: A Multi Agent Optimization Approach

2009 ◽  
Vol 19 (1) ◽  
pp. 1652-1662
Author(s):  
Minesh Poudel ◽  
Carlos A. Nunes Cosenza ◽  
Félix Mora-Camino
Keyword(s):  
Emergency Evacuation ◽  
Optimization Approach ◽  
Multi Agent
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