Ant Colony Optimization (ACO) is an acclaimed method for solving combinatorial problems proposed by Marco Dorigo in 1992 and has since been enhanced and hybridized many times. This paper proposes a novel modification of the algorithm, based on the introduction of a two-dimensional pheromone into a single-criteria ACO. The complex structure of the pheromone is supposed to increase ants’ awareness when choosing the next edge of the graph, helping them achieve better results than in the original algorithm. The proposed modification is general and thus can be applied to any ACO-type algorithm. We show the results based on a representative instance of TSPLIB and discuss them in order to support our claims regarding the efficiency and efficacy of the proposed approach.

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics.

Gas composition and light mode of industrial greenhouses are some of the most determining factors in the process of growing vegetable crops in greenhouse conditions. The intelligentisation of information technologies for monitoring and control based on artificial intelligence methods can increase the efficiency of agrotechnical procedures for greenhouse cultivation. One of the efficient approaches in today's world practice is the development and implementation of trustworthy hybrid decision-support systems based on the techniques of Fuzzy logic and Human-in-the-Loop. The research object is non-stationary processes of complex intelligent transformation of measurement data on the concentration of carbon dioxide and effective energy illumination in the growing zone of industrial greenhouses. The scientific novelty and practical value of the obtained research results consist in creating a computer model for aggregation and intelligent processing of agricultural monitoring data for greenhouses. The developed computer model is fully transformed into peripheral-level software of Internet of Things systems for agricultural purposes. This makes it possible to implement the for-computing architecture of monitoring systems in greenhouses. The obtained research results make it possible to optimise the resources used in growing crops in greenhouse conditions through the implementation of hardware and software intelligent monitoring tools that are adaptive to the types and periods of crop vegetation. The scientific and applied effect of the research is creating the novel approach of development and practical use of intelligent technologies for agrotechnical monitoring by substantiating the methodological provisions for synthesis of structural and algorithmic organisation of the corresponding software and hardware solutions.

Heating-cooling processes are widely used in industry and are often subject to latencies and delays. Although the parameters of such processes can be identified using mathematical and physical modeling, this technique is highly demanding both when it comes to the amount of a priori knowledge required and the time it takes to find a solution. In this work, we provide a robust formulation of a time-delay control problem and propose a numerical approach to finding its parameters in a form of an optimization task. Consequently, we apply various techniques and metaheuristics to obtain a high quality solution and discuss key observations made in the process.

Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model. Besides giving the background and the details of the proposed algorithms we apply them to optimization of selected variants of the problem and discuss the results.

Following the introduction of the socio-cognitive caste-based algorithms into the classic evolutionary metaheuristics, in this paper we focus on similar task regarding agent-based universal optimization methods. We tackle EMAS and DE algorithms and enrich them also with TOPSIS-inspired mechanism. Besides giving the details of the methods and the background, we present preliminary results after applying those techniques to solving the problem of optimization of time-delay system model.

Abstract Different hybrid optimization metaheuristics (see the works of Talbi for classification) either assume the embedding of one algorithm (usually a metaheuristic) in another (for instance, a local search inside an evolutionary algorithm—a memetic algorithm) or creating a chain of algorithms. In this paper, such a chain combination of two algorithms (namely, the Ant Colony Optimization and Evolutionary Algorithm) is presented. However, because of the intrinsic differences between the two algorithms (a vector of labels and a pheromone table when solving the traveling salesman problem, for example), several dedicated algorithms for translating the solutions between these two representations of the problem are proposed. The hybrid algorithm constructed with the application of the translation methods turns out to be significantly better in solving the TSP compared to non‐hybrid versions (relevant experimental results are presented and discussed). This paves the way for new possibilities of constructing hybrid metaheuristics by putting together completely different ones (using different representations); the impact of the presented research is aimed far beyond the hybridization of only ant colony optimization and evolutionary algorithm.

Summary Efficient information flow in the complex, often microscale simulation systems such as the social, artificial life, or traffic ones poses a significant challenge. It is difficult to implement a highly scalable system due to algorithmic problems, which significantly hamper the efficiency, especially in the case of maintaining a synchronized state in a parallelized, distributed environment. Our previous work presented a desynchronized method of information distribution in a simulation environment, inspired by the propagation of smell, and proved this method to be highly scalable. In this paper, we enhance and validate this method to ensure it does not invalidate the conclusions drawn from the simulation, enabling the development of efficient, scalable simulation systems. The prototype of the method presented here leverages the actor model for parallelization and cluster sharding mechanisms for cluster management, providing a comprehensive solution for large‐scale simulations, following realistic rules known from the nature. In order to validate the method of signal propagation modeling, three simulation models are created and tested. The validation is based on statistical analysis of metrics collected during the simulation execution. Statistical similarity of the results obtained from the distributed and nondistributed executions indicates that the distribution process does not impact the correctness of the simulation.

Recently there has been an increasing interest in telematics solutions for vehicles. These systems provide real-time danger detection, driving style evaluation or crash detection services. The provided information can significantly increase driving safety, help to improve driving style, support rescue operations and enable better-informed insurance pricing. While recently introduced vehicles often provide inbuilt telematics systems, older cars usually lack such functionalities. One option to remedy this issue is to develop telematics solutions that are based on sensors other than the original car equipment. In this paper we present our telematics solution that employs smartphone sensors. This research stems from our previous work on crash detection which we substantially expanded towards a system for automatic profiling of driving style, detection of driving anomalies, and community-based identification of dangerous places on public roads. We describe our approach to the processing of sensor data, driving profile construction, anomaly detection, and interaction with the driver, illustrating these aspects with selected tangible results.

Over a decade after the beginning of the multicore revolution, researchers and industry are still struggling with problems related to using concurrent hardware. Discovering or developing proper means for creating efficient, scalable, and adaptable software for multicore and multimode computers is still an open and a very important problem. Great efforts are made to solve problems related to the efficiency of resource utilization,1-3 monitoring and failure handling,4, 5 and, most importantly, development of highly concurrent systems.6-8 In addition to much work on adapting existing imperative technologies to the new challenges, we can observe a very interesting trend toward using the functional paradigm. The concepts of functional programming languages are very well suited to concurrent systems. Referential transparency, lazy evaluation, control over side-effects, immutable variables, and functions as first-class citizens help define maintainable and scalable solutions to the basic problems of parallel computing. The advantages of such an approach are clearly visible in HPC environments, where massively parallel solutions are needed. Providing novel services and solutions for the complex real-life problems of modern societies creates a growing demand for very fast solutions to computationally demanding problems. For example, planning complex urban road systems requires gathering results from large-scale simulations,9, 10 which are only feasible using massive parallelization. Similarly, in real-time scheduling tasks, complex optimization problems have to be solved within a single second.11, 12 Such challenges can greatly benefit from technologies based on the functional paradigm, which simplify the development process and lower the barrier for utilizing modern HPC hardware. In this special section, we have three interesting papers focusing on different issues arising in highly concurrent and distributed systems. They all adopt the functional paradigm and show its potential to solve problems of efficient utilization of available hardware. The first paper, authored by Krzywicki et al,8 tackles concurrent computations expressed using the agent paradigm. The paper introduces a new formal description of the execution model for agent-based computing systems in the form of an adaptive dataflow decoupled from the domain-specific semantics of the computation. The authors have shown that the execution models studied in previous work can be unified in this common model. The parameters of the model, such as queuing policies and granularity of the data in the flow are analyzed. Several queueing alternatives are benchmarked to demonstrate how they affect the efficiency of the computation. Using the example of a multi-agent evolutionary optimization problem solver, the new approach is shown to outperform the classic one. This proposed model is well suited to functional languages and can be easily mapped onto different classes of hardware—from simple single-core computers to distributed environments. The second paper, authored by Berenyi et al,3 focuses on linear algebraic expressions, stating that they are the essence of many computationally intensive problems, including scientific simulations and machine learning applications. However, translating high-level formulations of these expressions to efficient machine-level representations is far from trivial: developers should be assisted by automatic optimization tools so that they can focus their attention on high-level problems rather than low-level details. The tractability of these optimizations is highly dependent on the choice of the primitive constructs in terms of which the computations are expressed. In this work, the authors propose describing operations on multi-dimensional arrays using a selection of higher-order functions, inspired by functional programming, and present rewrite rules for these that can optimize them automatically for modern hierarchical and heterogeneous architectures. Using this formalism, the authors systematically construct and analyze different subdivisions and permutations of the dense matrix multiplication problem. The final paper, authored by Ciolczyk et al,5 focuses on the problem of tracing agent-based systems at large scale. At scale, assuming of course that the system considered is distributed, there are situations where processing messages within one of the actors fails—often due to failures that had occurred earlier in the system. Tracking down the origin of the failure can be difficult since existing monitoring tools only provide ways to collect metrics and statistical information about system execution. In this paper, the authors describe a new tool for tracing distributed actor systems—the Akka Tracing Tool—which allows users to generate a trace graph of messages. To address the distributed nature of the environment, the authors propose an efficient data collection mechanism based on the one-way replication technique implemented in CouchDB—a popular document database. The tool was evaluated in a distributed environment of up to 50 nodes, set up in the Amazon Web Services computing cloud, on a real application: car traffic simulation. The overhead measured when tracing all messages was between 39% to 45% on average. The library also proved to be scalable with respect to the number of nodes in the actor system and to be user-friendly. Thanks to these properties, the authors expect that the tool can simplify finding errors and speed up the development process of actor systems. The renaissance of the functional paradigm is a fact—languages like Haskell and Erlang find more and more industrial applications, and functional concepts are being introduced into classic object-oriented technologies such as Java and .NET. While this trend may not result in a complete paradigm-shift, “paradigm-mix” is already well established. The concepts inspired by functional approach help in building highly concurrent systems in various technologies. Nevertheless, many problems of massive parallelism still remain open. We believe that the work presented in this special section is a valuable step toward methods for building efficient, scalable, and robust software for modern multicore and multimode hardware platforms.

Memetic algorithms are a type of genetic algorithms very valuable in optimization problems. They are based on the concept of “meme”, and use local search techniques, which allow them to avoid premature convergence to suboptimal solutions. Among these algorithms we can consider Lamarckian and Baldwinian models, depending on whether they modify (the former) or not (the latter) the agent’s genotype. In this paper we analyze the application of memetic algorithms to the NPerson Iterated Prisoner’s Dilemma (NIPD). NIPD is an interesting game that has proved to be very useful to explore the emergence of cooperation in multi-player scenarios. The main contributions of this paper are related to setting the ground to understand the implications of the memetic model and the related parameters. We investigate to which extent these decisions determine the level of cooperation obtained as well as the memory and the execution performance.

In the paper an application of selected agent-based evolutionary computing models, such as flock-based multi agent system (FLOCK) and evolutionary multi-agent system (EMAS), to the problem of continuous optimisation is presented. It turns out, that hybridizing of agent-based paradigm with evolutionary computation brings a new quality to the meta-heuristic field, easily enhancing static individuals with possibilities of perception and interaction with other agents. The examination of selected benchmarks leads to the observation regarding the overall efficiency of the systems in comparison to the standard genetic algorithm (as defined by Michalewicz) and memetic versions of all the systems. The experiments confirm that the efficiency is dependent on the problem, however, the observed number of fitness function calls makes EMAS dominate over its competitors. This feature makes EMAS a promising solution for the problems with complex fitness functions, (such as inverse problems).

Existing solutions for agent-based systems turn out to be limited in some applications, like agent-based computing or simulations, where very large numbers of clearly defined agents interact heavily within a closed system. In those cases, fully-fledged, FIPA1 compliant environment introduce unnecessary overhead, but simple tools fail to scale when confronted to bigger problems. In this paper, we introduce an alternative agent environment called AgE, targeted at medium-sized simulation and computational applications, which use multi-agent and computational intelligence paradigms, but does not need full FIPA compliancy, and would benefit from a component-based approach and distributed computing capabilities. After giving a short review of selected popular multi-agent platforms, the main features of AgE are presented. Next, some basic usability topics are addressed. Then the most interesting architectural aspects of the platform are discussed. Finally, AgE possibilities are demonstrated with two example applications. Keywords— agent-based computing, component-based systems, agent-based simulation

In the paper an application of hybridized Evolutionary Multi-Agent System (EMAS) with local search (in memetic style) to the problem of continuous optimisation is presented. Before, the concept of evolutionary and memetic agent-based computing is given, the former being a computing paradigm researched for over 15 years, the latter being introduced recently. Two ways of memetic hybridization (Lamarckian and Baldwinian) are discussed, and examined in the course of experiments. In the presented experiments, evolutionary and memetic multi-agent systems are compared with classical evolutionary algorithm (Michalewicz model) implemented with allopatric speciation (island-model of evolutionary algorithm), based on a selected popular benchmark continuous optimization functions.

The paper presents an idea of agent-based meta-heuristic integrating a computational optimization system (evolutionary multi-agent system) with ant colony optimization technique. In the proposed model, chosen parameters of ant colonies may be encoded as genotypes and subjected to evolution process carried out by agents. The goal of the whole system is to search for the best solution of the discrete optimization problem based on the results of the ant colonies run using different parameters. The proposed concept forms a base for further research on bringing different interactions known in ant-colony optimization to the inter-agent level. The considerations are illustrated with preliminary experimental results obtained for parallel ant system solving quadratic assignment problem.

Agent-based framework dedicated to acquiring and processing heterogeneous data, collected in various Internet sources is presented. It is built upon a hierarchical, distributed computation system Age that has already been successfully used for various optimization and classification task.

The model for the biologically inspired agent-based computation systems EMAS and iEMAS conformed to BDI standard is presented. System dynamics was modeled as the stationary Markov chain. The space of states and transition functions were identified. The probability transition of the whole system is composed of the conditional transitions caused by the particular actions. Such a model allows for better understanding the behavior of the proposed complex systems as well as their limitations. Because no constraint for the total number of agents was introduced, the model express the behavior of maximum configuration of the systems. Therefore it plays the similar role to the SGA infinite population model introduced by Vose. The sample application of iEMAS to the difficult global optimization problem (optimization of the artificial neural network architecture) showing its efficiency was also attached.

The mathematical model of the biologically inspired, memetic, agent-based computation systems EMAS and iEMAS conformed to BDI standard is presented. The state of the systems and their dynamics are expressed as stationary Markov chains. Such an approach allows to better understand their complex behavior as well as their limitations.

The refined model for the biologically inspired agent-based computation systems EMAS and iEMAS conforming to the BDI standard is presented. Moreover, their evolution is expressed in the form of the stationary Markov chains. This paper generalizes the results obtained by Byrski and Schaefer [7] to a strongly desired case in which some agents' actions can be executed in parallel. In order to find the Markov transition rule, the precise synchronization scheme was introduced, which allows to establish the stepwise stochastic evolution of the system. The crucial feature which allows to compute the probability transition function in case of parallel execution of local actions is the commutativity of their transition operators. Some abstract conditions expressing such a commutativity which allow to classify the agents' actions as local or global are formulated and verified in a very simple way. The above-mentioned Markov model constitutes the basis of the asymptotic analysis of EMAS and iEMAS necessary to evaluate their search possibilities and efficiency.