Evolutionary Multi-agent System Research Articles

(µ + λ) Evolution Strategy with Socio-cognitive Mutation

Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on sociocognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.

MoTrans-BDI: Leveraging the Beliefs-Desires-Intentions agent architecture for collaborative model transformation by example

Due to the growing interest in model-driven system development techniques, the efficient design of automated model transformations between heterogeneous models has become a major challenge in software development. While a number of specialized languages have been proposed, aiming at specifying model transformations, there is currently no matured foundation for specifying transformations between such models that are based on designers/experts collaboration in order to propose a solution that satisfies users’ requirements and application constraints. This transformation process is a complex task and must emulate how designers and experts with different perspectives behave and reflect about model transformations. In this paper, we propose a framework based on a novel approach for the specification and design of model transformations called MoTrans-BDI, which leverages Evolutionary Multi-Agent System (EMAS) to simulate designers’ expertise for the transformation of models. Our approach is based on the Belief-Desire-Intention (BDI) agent model and the Contract Net Protocol where agents’ beliefs feed from a series of transformation examples. The emphasis of using the specific model is the opportunity to produce a target model that may be composed of parts from different experts’ designs. We first experimentally evaluate MoTrans-BDI on twelve handmade UML2REL model transformation problems. All types of agents are able to produce perfect target models compared to human experts’ collaboratively-produced target models. Second, we empirically evaluate MoTrans-BDI’s proposal in terms of response time on Star Schema Benchmark (SSB) queries. An application of MoTrans-BDI in the case of UML2REL transformation is performed to stress and highlight the transformation process steps.

Applying autonomous hybrid agent-based computing to difficult optimization problems

Evolutionary multi-agent systems (EMASs) are very good at dealing with difficult, multi-dimensional problems, their efficacy was proven theoretically based on analysis of the relevant Markov-Chain based model. Now the research continues on introducing autonomous hybridization into EMAS. This paper focuses on a proposed hybrid version of the EMAS, and covers selection and introduction of a number of hybrid operators and defining rules for starting the hybrid steps of the main algorithm. Those hybrid steps leverage existing, well-known and proven to be efficient metaheuristics, and integrate their results into the main algorithm. The discussed modifications are evaluated based on a number of difficult continuous-optimization benchmarks.

Design and implementation of parallel self-adaptive differential evolution for global optimization

Abstract The results of evolutionary algorithms depend on population diversity that normally decreases by increasing the selection pressure from generation to generation. Usually, this can lead the evolution process to get stuck in local optima. This study is focused on mechanisms to avoid this undesired phenomenon by introducing parallel self-adapted differential evolution that decomposes a monolithic population into more variable-sized sub-populations and combining this with the characteristics of evolutionary multi-agent systems into a hybrid algorithm. The proposed hybrid algorithm operates with individuals having some characteristics of agents, e.g. they act autonomously by selecting actions, with which they affect the state of the environment. Additionally, this algorithm incorporates two additional mechanisms: ageing and adaptive population growth, which help the individuals by decision-making. The proposed parallel differential evolution was applied to the CEC’18 benchmark function suite, while the produced results were compared with some traditional stochastic nature-inspired population-based and state-of-the-art algorithms.

Energy redistribution in autonomous hybridization of agent-based computing

Evolutionary multi-agent systems (EMAS) are very good at dealing with difficult, multi-dimensional problems. Currently, research is underway to improve this algorithm, giving even more freedom to agents not only in solving the problem but also in making decisions on the behavior of the algorithm. One way is to hybridize this algorithm with other existing algorithms creating Hybrid Evolutionary Multi Agent-System (HEMAS). Unfortunately, such connections generate problems in the form of an unbalanced energy level of agents who have made the decision to use such an improvement. One of the solutions is the mechanism of redistributing the agents' energy in the form of an operator. The article presents several proposals of redistribution operators along with numerous experimental results.

Tendencies in Multi-Agent Systems: A Systematic Literature Review

The application of Artificial Intelligence mechanisms allows the development of systems capable to solve very complex engineering problems. Multi-agent systems (MAS) are one paradigm that allows an alternative way to design distributed control systems. While research in this area grew exponentially before 2009, there is a need to understand the status quo of the field from 2009 to June 2017. An extension of the results of a SLR related to Multi-Agent Systems, its applications and research gaps, following Kitchenham and Wholin guidelines are presented in this paper. From the analysis of 279 papers (out of 3522 candidates), our findings suggest that: a) there were 20 gaps related to agent-oriented methodologies; coordination, cooperation and negotiation; modelling, developing, testing and debugging; b) 24 gaps related to specific domains (recycling, dynamic evacuation, hazard management, health-care, industry, logistics and manufacturing, machine learning, ambient assisted living); and 14 gaps related to specific areas within MAS (A-Teams, dynamic MAS and mobile agents, ABMS, evolutionary MAS, and self-organizing MAS). These gaps specify lines of research where the MAS community must work to achieve the unification of the agent-oriented paradigm; as well as strengthen ties with the industry.

Evolutionary Multi-Agent System with Crowding Factor and Mass Center mechanisms for Multiobjective Optimisation

This work presents some additional mechanisms for Evolutionary Multi-Agent Systems for Multiobjective Optimisation trying to solve problems with population stagnation and loss of diversity. Those mechanisms reward solutions located in a less crowded neighborhood and on edges of the frontier. Both techniques have been described and also some preliminary results have been shown.

The Missing Link! A New Skeleton for Evolutionary Multi-agent Systems in Erlang

Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton in Erlang for parallel EMAS computations. The skeleton enables us to capture a wide variety of concrete evolutionary computations that can exploit the same underlying parallel implementation. We demonstrate the use of our skeleton on two different evolutionary computing applications: (1) computing the minimum of the Rastrigin function; and (2) solving an urban traffic optimisation problem. We show that we can obtain very good speedups (up to 142.44times the sequential performance) on a variety of different parallel hardware, while requiring very little parallelisation effort.

Agent-based Evolutionary and Memetic Black-box Discrete Optimization

Hybridizing agent-based paradigm with evolutionary or memetic computation can enhance the field of meta-heuristics in a significant way, giving to usually passive individuals autonomy and capabilities of perception and interaction with other ones. In the article, an evolutionary multi-agent system (EMAS) is applied to solve difficult discrete benchmark problems without any domain-specific knowledge—thus they may be called “black-box” ones. As a means for comparison, a parallel evolutionary algorithm (constructed along with Michalewicz model) versus evolutionary and memetic versions of EMAS are used. The obtained results point out that EMAS is significantly more efficient than classical evolutionary algorithms and also finds better results in the examined problem instances.

Hybridization of Isogeometric Finite Element Method and Evolutionary Multi-agent System as a Tool-set for Multiobjective Optimization of Liquid Fossil Fuel Reserves Exploitation with Minimizing Groundwater Contamination

In the paper we consider the approach for solving the problem of extracting liquid fossil fuels respecting not only economical aspects but also the impact on natural environment. We model the process of extracting of the oil/gas by pumping the chemical fluid into the formation with the use of IGA-FEM solver as non-stationary flow of the non-linear fluid in heterogeneous media. The problem of extracting liquid fossil fuels is defined as a multiobjective one with two contradictory objectives: maximizing the amount of the oil/gas extracted and minimizing the contamination of the groundwater. The goal of the paper is to check the performance of a hybridized solver for multiobjective optimization of liquid fossil fuel extraction (LFFEP) integrating population-based heuristic (i.e. evolutionary multi-agent system and NSGA-II algorithm for approaching the Pareto frontier) with isogeometric finite element method IGA-FEM. The results of computational experiments illustrate how the considered techniques work for a particular test scenario.

Efficient Memetic Continuous Optimization in Agent-based Computing

This paper deals with a concept of memetic search in agent-based evolutionary computation. In the presented approach, local search is applied during mutation of an agent. Using memetic algorithms causes increased demand on the computing power as the number of fitness function calls increases, therefore careful planning of the fitness computing (through the proposed local search mechanism based on caching parts of the fitness function) leads to significant lowering of this demand. Moreover, applying local search with care, can lead to gradual improvement of the whole population. In the paper the results obtained for selected high-dimensional (5000 dimensions) benchmark functions are presented. Results obtained by the evolutionary and memetic multi-agent systems are compared with classic evolutionary algorithm.

Massively concurrent agent-based evolutionary computing

The fusion of the multi-agent paradigm with evolutionary computation yielded promising results in many optimization problems. Evolutionary multi-agent systems (EMAS) are more similar to biological evolution than classical evolutionary algorithms. However, technological limitations prevented the use of fully asynchronous agents in previous EMAS implementations. In this paper we present a new algorithm for agent-based evolutionary computations. The individuals are represented as fully autonomous and asynchronous agents. An efficient implementation of this algorithm was possible through the use of modern technologies based on functional languages (namely Erlang and Scala), which natively support lightweight processes and asynchronous communication. Our experiments show that such an asynchronous approach is both faster and more efficient in solving common optimization problems.

Evolutionary multi-agent systems

AbstractThe aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS). The paper starts with a general introduction describing the background, structure and behaviour of EMAS. EMAS application to solving global optimisation problems is presented in the next section along with its modification targeted at lowering the computation costs by early removing certain agents based on immunological inspirations. Subsequent sections deal with the elitist variant of EMAS aimed at solving multi-criteria optimisation problems, and the co-evolutionary one aimed at solving multi-modal optimisation problems. Each variation of EMAS is illustrated with selected experimental results.

Query join ordering optimization with evolutionary multi-agent systems

This work presents an evolutionary multi-agent system applied to the query optimization phase of Relational Database Management Systems (RDBMS) in a non-distributed environment. The query optimization phase deals with a known problem called query join ordering, which has a direct impact on the performance of such systems. The proposed optimizer was programmed in the optimization core of the H2 Database Engine. The experimental section was designed according to a factorial design of fixed effects and the analysis based on the Permutations Test for an Analysis of Variance Design. The evaluation methodology is based on synthetic benchmarks and the tests are divided into three different experiments: calibration of the algorithm, validation with an exhaustive method and a general comparison with different database systems, namely Apache Derby, HSQLDB and PostgreSQL. The results show that the proposed evolutionary multi-agent system was able to generate solutions associated with lower cost plans and faster execution times in the majority of the cases.

Computing agents for decision support systems

In decision support systems, it is essential to get a candidate solution fast, even if it means resorting to an approximation. This constraint introduces a scalability requirement with regard to the kind of heuristics which can be used in such systems. As execution time is bounded, these algorithms need to give better results and scale up with additional computing resources instead of additional time. In this paper, we show how multi-agent systems can fulfil these requirements. We recall as an example the concept of Evolutionary Multi-Agent Systems, which combines evolutionary and agent computing paradigms. We describe several possible implementations and present experimental results demonstrating how additional resources improve the efficacy of such systems.

Generation-free Agent-based Evolutionary Computing

Metaheuristics resulting from the hybridization of multi-agent systems with evolutionary computing are efficient in many optimization problems. Evolutionary multi-agent systems (EMAS) are more similar to biological evolution than classical evolutionary algorithms. However, technological limitations prevented the use of fully asynchronous agents in previous EMAS implementations. In this paper we present a new algorithm for agent-based evolutionary computations. The individuals are represented as fully autonomous and asynchronous agents. Evolutionary operations are performed continuously and no artificial generations need to be distinguished. Our results show that such asynchronous evolutionary operators and the resulting absence of explicit generations lead to significantly better results. An efficient implementation of this algorithm was possible through the use of Erlang technology, which natively supports lightweight processes and asynchronous communication.

Agent-based Evolutionary Computing for Difficult Discrete Problems

Hybridizing agent-based paradigm with evolutionary computation can enhance the field of metaheuristics in a significant way, giving to usually passive individuals autonomy and capabilities of perception and interaction with other ones, treating them as agents. In the paper as a follow-up to the previous research, an evolutionary multi-agent system (EMAS) is examined in difficult discrete benchmark problems. As a means for comparison, classical evolutionary algorithm (constructed along with Michalewicz model) implemented in island-model is used. The results encourage for further research regarding application of EMAS in discrete problem domain.

A Self-Learning Network Reconfiguration Using Fuzzy Preferences Multi-Objective Approach

The paper proposes a self-learning evolutionary multi-agent system for distribution network reconfiguration. The network reconfiguration is modeled as a multi-objective combinational optimization. An autonomous agent-entity cognizes the physical aspects as operational states of the local substation, the agent-entities establish relationship network based on the interactions to provide service. Multiple objectives are considered for load balancing among the feeders, minimum deviation of the nodes voltage, minimize the power loss and branch current constraint violation. These objectives are modeled with fuzzy sets to evaluate their imprecise nature and one can provide the anticipated value of each objective. The method completes the network reconfiguration based on the negotiation of autonomous agent-entities. Simulation results demonstrated that the proposed method is effective in improving performance. DOI: http://dx.doi.org/10.11591/telkomnika.v11i2.1997

Niching in Evolutionary Multi-agent Systems

Niching is a group of techniques used in evolutionary algorithms, useful inseveral types of problems, including multimodal or nonstationary optimiza-tion. This paper investigates the applicability of these methods to evolutionarymulti-agent systems (EMAS), a hybrid model combining the advantages of evo-lutionary algorithms and multi-agent systems. This could increase the efficiencyof this type of algorithms and allow to apply them to a wider class of prob-lems. As a starting point, a simple but flexible EMAS framework is proposed.Then, it is shown how to extend this framework in order to introduce niching,by adapting two classical niching methods. Finally, preliminary experimentalresults show the efficiency and the simultaneous discovery of multiple optimaby this modified EMAS.

Evolutionary Multi-Agent Systems in Non-Stationary Environments

In the article the performance of an evolutionary multi-agent system in dynamic optimization is evaluated in comparison to classical evolutionary algorithms. The starting point is a general introduction describing the background, structure and behaviour of EMAS against classical evolutionary techniques. Then the properties of energy-based selection are investigated to show how it may influence the diversity of the population in EMAS. The considerations are illustrated by experimental results based on the dynamic version of the well-known, high-dimensional Rastrigin function benchmark.