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One of the key tasks in the economy is forecasting the economic agents' expectations of the future values of economic variables using mathematical models. The behavior of mathematical models can be irregular, including chaotic, which reduces their predictive power. In this paper, we study the regimes of behavior of two economic models and identify irregular dynamics in them. Using these models as an example, we demonstrate the effectiveness of evolutionary algorithms and the continuous deep Q-learning method in combination with Pyragas control method for deriving a control action that stabilizes unstable periodic trajectories and suppresses chaotic dynamics. We compare qualitative and quantitative characteristics of the model's dynamics before and after applying control and verify the obtained results by numerical simulation. Proposed approach can improve the reliability of forecasting and tuning of the economic mechanism to achieve maximum decision-making efficiency., Comment: 12 pages, 4 figures

Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudorandom number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions. Web of Science 587 719 692

In modern applications, large graphs are usually applied in the simulation and analysis of large complex systems such as social networks, computer networks, maps, traffic networks. Therefore, graph mining is also an interesting subject attracting many researchers. Among them, frequent subgraph mining in a single large graph is one of the most important branches of graph mining, it is defined as finding all subgraphs whose occurrences in a dataset are greater than or equal to a given frequency threshold. In which, the GraMi algorithm is considered the state of the art approach and many algorithms have been proposed to improve this algorithm. In 2020, the SoGraMi algorithm was proposed to optimize the GraMi algorithm and presented an outstanding performance in terms of runtime and storage space. In this paper, we propose a new algorithm to improve SoGraMi based on connected components, called CCGraMi (Connected Components GraMi). Our experiments on four real datasets (both directed and undirected) show that the proposed algorithm outperforms SoGraMi in terms of running time as well as memory requirements.

In this chapter, we introduce the basic characteristics of swarm intelligence, the path planning problem for robots, and how to apply the self-organizing migrating algorithm, a representative of swarm intelligence to solve that real-world problem. We set up simulations in the Matlab environment with four common possible scenarios to demonstrate the effectiveness of the solution.

With the rapid growth of technology in the digital landscape, cybercriminals attempt to utilize new and sophisticated techniques to autonomous and increase the speed and scale of their attacks. Meanwhile, the Dark Web infrastructures such as Tor, plays a crucial role in the criminal underground, especially for malware developers' communities. It is logical to expect that the malicious actors would utilize the combination of these techniques in shortcoming time. To better understand the upcoming threat, in this manuscript, we investigate the design and mitigation of such malware. Accordingly, we introduce X-sWarm, which will be the next generation of resilient, stealthy malware that leverages the intelligent technique and the darknet infrastructures. Furthermore, we show that with the self-healing network mechanism, X-sWarm can achieve a low diameter and a low degree and be robust to partitioning under node removal. More importantly, we suggest the mitigation technique that neutralizes the nodes of the proposed worm.

Along with the rapid increase in the popularity of online media, the proliferation of fake news and its propagation is also rising. Fake news can propagate with an uncontrollable speed without verification and can cause severe damages. Various machine learning and deep learning approaches have been attempted to classify the real and the false news. In this research, the author group presents a comprehensive performance evaluation of eleven supervised algorithms on three datasets for fake news classification.

In this article, we describe the design and implementation of a variant version of SOMA named SOMA Pareto to solve ten hard problems of the 100-Digit Challenge. The algorithm consists of the following operations: Organization, Migration, and Update. In which, we focus on improving the Organization operation with the adaptive parameters of PRT and Step. When applying the SOMA Pareto to solve ten hard problems to 10 digits of accuracy, we achieved a competitive result: 85.04 points.

The paper proposes a method for the drone to catch the given target and avoid detected obstacles in its path based on the self-organizing migrating algorithm. In particular, a two-component fitness function is proposed based on the principle that the closer the target, the lower the fitness value, and the closer the obstacle, the higher the fitness value. Self-organizing migrating algorithm, a swarm intelligence algorithm, is used to predict the next positions that the drone will move to. These positions both satisfy the requirement to avoid obstacles and shorten the distance to the target. A map of two drones, two corresponding targets and four static obstacles was modeled on Matlab. The simulation results verify the correctness and effectiveness of the proposed method.

In this paper, we address 10 basic test functions of the 100-Digit Challenge of the SEMCCO 2019 & FANCCO 2019 Competition by using team-to-team adaptive seft-organizing migrating algorithm - SOMA T3A with many improvements in the Organization, Migration, and Update process, as well as the linear adaptive PRT and the cosine-based adaptive Step. The results obtained from the algorithm on the 100-Digit Challenge are very promising with 93 points in total.

This article introduces a version of the Self-Organizing Migrating Algorithm with a narrowing search space strategy named iSOMA. Compared to the previous two versions, SOMA T3A and Pareto that ranked 3rd and 5th respectively in the IEEE CEC (Congress on Evolutionary Computation) 2019 competition, the iSOMA is equipped with more advanced features with notable improvements including applying jumps in the order, immediate update, narrowing the search space instead of searching on the intersecting edges of hyperplanes, and the partial replacement of individuals in the population when the global best improved no further. Moreover, the proposed algorithm is organized into processes named initialization, self-organizing, migrating, and replacement. We tested the performance of this new version by using three benchmark test suites of IEEE CEC 2013, 2015, and 2017, which, together contain a total of 73 functions. Not only is it superior in performance to other SOMAs, but iSOMA also yields promising results against the representatives of well-known algorithmic families such as Differential Evolution and Particle Swarm Optimization. Moreover, we demonstrate the application of iSOMA for path planning of a drone, while avoiding static obstacles and catching the target. Web of Science 116 art. no. 108270

In this paper, we apply the SOMA T3A algorithm to solve 10 hard problems of the 100-Digit Challenge of the GECCO 2019 Competition. With effective improvements in choosing Migrants and Leader in the organization process, as well as the Step and PRT adaptive parameters in the migration process, the algorithm has achieved promising results. The total score that the algorithm achieved is 92.04 points.

This paper presents a method for swarm robots catching multiple moving targets without colliding any dynamic obstacles and other robots in an unknown complex environment. An imaginary map, including multi-layers corresponding to the number of robots, is built in which the starting position, the target, the obstacles, and the robot denoted by the highest position, the lowest position, the small hills, and the spherical ball on the map. The PSO algorithm was proposed to lead the robot to move on the map toward the given targets safely. Simulation results are also presented to show the feasibility of the method.

This research represents a detailed insight into the modern and popular hybridization of unconventional quasiperiodic/chaotic sequences and evolutionary computation. It is aimed at the influence of different randomization schemes on the population diversity, thus on the performance, of two selected adaptive Differential Evolution (DE) variants. Experiments are focused on the extensive investigation of totally ten different randomization schemes for the selection of individuals in DE algorithm driven by the default pseudo-random generator of Java environment and nine different two-dimensional discrete chaotic systems, as the unconventional chaotic pseudo-random number generators. The population diversity is recorded for 15 test functions from the CEC 2015 benchmark set in 10D.

This paper presents a method for swarm robot to catch the moving target and to avoid multiple dynamic obstacles in the unknown environment. An imaginary map is built, including the highest mountain, some small hills, and a lowest lying land, respectively corresponding to the starting position of the robot, the detected obstacles, and the target. The robot is considered as a flow of water flowing from high to low. The flow of water is the robot trajectory that is divided into a set of points created by an algorithm called Self-organizing migrating algorithm. Simulation results are also presented to show that the obstacle avoidance and catching target task can be reached using this method.