Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

Aydemir, Fatma Başak; Günay, Akın; Oztoprak, Figen; Birbil, Ş. İlker; Yolum, Pınar

doi:10.1007/s10898-012-0012-3

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…The system terminates when a persistent solution appears. Recently, Aydemir et al (2013) proposed a multi-agent cooperation for solving global optimization problems through the introduction of a new multi-agent environment, MANGO.…”

Section: Literature Review and Description Of Multi-agent Optimizationmentioning

confidence: 99%

Homogenous multi-agent optimization for process systems engineering problems with a case study of computer aided molecular design

Gebreslassie

Diwekar

2017

Chemical Engineering Science

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In this paper, we propose a novel homogenous multi-agent optimization (HMAO) framework for optimal design of large scale process system engineering problems. The platform is validated using a benchmark problems and a computer-aided molecular design (CAMD) problem. The molecular design problem is a solvent selection problem and it is formulated as a mixed integer nonlinear programming (MINLP) in which solute distribution coefficient of a candidate solvent is maximized subject to structural feasibility, thermodynamic property and process constraints. The model simultaneously determines the optimal decisions that include the size and the functional groups of the candidate solvents. In developing the HMAO framework, multiple efficient ant colony optimization (EACO) algorithms are considered as distinct algorithmic agents. We illustrate this approach through a real world case study of the optimal design of solvent for extraction of acetic acid from waste process stream using liquid-liquid extraction. The UNIFAC model based on the infinite dilution activity coefficient is used to estimate the mixture properties. The results show that quality of the objective function and the computational efficiencies are improved by a factor ranged from 1.475 to 4.137. The new solvents proposed in this work are with much better targeted thermodynamic properties compared to the solvents proposed so far in previous studies.

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Section: Literature Review and Description Of Multi-agent Optimizationmentioning

confidence: 99%

Homogenous multi-agent optimization for process systems engineering problems with a case study of computer aided molecular design

Gebreslassie

Diwekar

2017

Chemical Engineering Science

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“…Also, a parallel DE based on GPUs is explored in [44], which employs self-adapting control parameters and generalized opposition-based learning (GOBL) to improve the quality of candidate solutions. In [45] a multiagent framework was proposed to create a distributed cooperative structure based on agents. This scheme was implemented in Java, defining a communication API (Application Programming Interface) to send information to the different agents of the environment.…”

Section: Related Workmentioning

confidence: 99%

Enhanced parallel Differential Evolution algorithm for problems in computational systems biology

Penas

Banga

González

et al. 2015

Applied Soft Computing

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“…Another coordination-and cooperation-based multiagent system, named MANGO (Aydemir et al 2013), was proposed for solving global optimization problems. MANGO is a Javabased multiagent framework implemented by APIs capable of running on different machines and share the results based on message passing mechanism.…”

Section: State-of-the-art In Multiagent Systems For Optimizationmentioning

confidence: 99%

“…The first type manages JMS communication resources and the second type is the directory service. MANGO can use any of optimization algorithms for the agents and the agent designer decides which algorithm should be applied (Aydemir et al 2013). The authors of MANGO did not provide a detailed test of the system using hard numerical optimization benchmarks; hence its success for practical cases is not known.…”

Section: State-of-the-art In Multiagent Systems For Optimizationmentioning

confidence: 99%

A tournament-based competitive-cooperative multiagent architecture for real parameter optimization

Lotfi

Acan

2015

Soft Comput

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Real parameter optimization is an important task in almost all engineering applications. This paper introduces a novel multiagent architecture and agent interaction mechanism for the solution of single objective type realparameter optimization problems. The proposed multiagent system includes several metaheuristics as problem-solving agents that act on a common population containing the frontiers of search process and a common archive keeping the promising solutions extracted so far. Each session of the proposed architecture includes two phases: a tournament among all agents to determine the currently best performing agent and a search procedure conducted by the winner. In the tournament phase, each agent performs a fixed number of fitness evaluations over the common population and gets a success score in terms the fitness improvements it achieved by itself. The agent with the best score is the winner of the tournament. Then, the winner agent is allowed to conduct its search algorithm using the common population until its procedure gets stuck at a locally optimal solution or maximum fitness evaluations per session is reached. Afterwards, the procedure restarts with another tournament to determine the next winner. In all phases and iterations of the proposed framework, all agents use the same population and archive in conducting their search procedures. This way, agents compete with each other in terms of their fitness improvements achieved over a fixed number of fitness evaluations in tournaments, and Communicated by V. Loia.B Adnan Acan they cooperate by sharing their search experiences through accumulating them in a common population and a common archive. The proposed multiagent system is experimentally evaluated using the well-known CEC2005 benchmark problems set. Analysis of the obtained results exhibited that the proposed framework performs significantly better than its state-of-the-art competitors in almost all problem instances.

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Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

Cited by 7 publications

References 30 publications

Homogenous multi-agent optimization for process systems engineering problems with a case study of computer aided molecular design

Homogenous multi-agent optimization for process systems engineering problems with a case study of computer aided molecular design

Enhanced parallel Differential Evolution algorithm for problems in computational systems biology

A tournament-based competitive-cooperative multiagent architecture for real parameter optimization

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