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Weapon-target assignment (WTA) is a kind of NP-complete problem in military operations research. To solve the multilayer defense WTA problems when the information about enemy&rsquo ; s attacking plan is symmetric to the defender, we propose four heuristic algorithms based on swarm intelligence with customizations and improvements, including ant colony optimization (ACO), binary particle swarm optimization (BPSO), integer particle swarm optimization (IPSO) and sine cosine algorithm (SCA). Our objective is to assess and compare the performance of different algorithms to determine the best algorithm for practical large-scale WTA problems. The effectiveness and performance of various algorithms are evaluated and compared by means of a benchmark problem with a small scale, the theoretical optimal solution of which is known. The four algorithms can obtain satisfactory solutions to the benchmark problem with high quality and high robustness, while IPSO is superior to BPSO, ACO and SCA with respect to the solution quality, algorithmic robustness and computational efficiency. Then, IPSO is applied to a large-scale WTA problem, and its effectiveness and performance are further assessed. We demonstrate that IPSO is capable of solving large-scale WTA problems with high efficiency, high quality and high robustness, thus meeting the critical requirements of real-time decision-making in modern warfare.

Aquest article critica el deferencialisme científic, que està basat en una comprensió errònia de la ciència i ha fonamentat les crítiques actuals a la democràcia com a procés de presa de decisions col·lectives. Plató, un dels primers a veure la democràcia com a problema, va pensar que la ignorància i irracionalitat de la majoria justificava el govern dels savis. Brennan, en Against Democracy (2016), ha defensat el mateix. L'autor ofereix, per contra, una alternativa innovadora basada en una concepció fal·libilista del coneixement i la democràcia segons la qual l'autoritat i legitimitat de la democràcia no depenen de la seva tendència a prendre solucions encertades, sinó de la tendència de les multituds intel·ligents en l'era de la Informació a evitar decisions equivocades. ; This paper criticizes scientific deferentialism. This deferentialism is based on an erroneous understanding of science, but it has substantiated the current criticism of democracy as a process of collective decision-making. Plato, one of the earliest to see democracy as a problem, thought that the ignorance and the irrationality of the many justified the government of the wise. Brennan, in Against Democracy (2016), defended the same. The author offers, on the other hand, an innovative alternative based on a fallibilistic conception of knowledge and democracy, according to which democratic authority and legitimacy do not depend on the tendency of democracy to made correct solutions, but on the tendency of intelligent crowds, in the Information Age, to avoid wrong decisions. ; Este artículo critica el deferencialismo científico. Este está basado en una errónea comprensión de la ciencia, pero ha fundamentado las críticas actuales a la democracia como proceso de toma de decisiones colectivas. Platón, uno de los primeros en ver la democracia como problema, pensó que la ignorancia e irracionalidad de la mayoría justificaba el gobierno de los sabios. Brennan, en Against Democracy (2016), ha defendido lo mismo. El autor ofrece, por el contrario, una alternativa innovadora basada en una concepción falibilista del conocimiento y la democracia, según la cual la autoridad y legitimidad de la democracia no dependen de su tendencia a tomar soluciones acertadas, sino de la tendencia de las multitudes inteligentes en la era de la información a evitar decisiones equivocadas.

In social networks, controversy, provocation, and incitement mutate quickly to an explosive mixture. Journalists, who aim for a factual moderation, are often highly frustrated to meet the criticism of trolls and haters. The essay addresses the following questions: How can newsrooms cope with the massively growing feedback from users? What responsibility carries the media and the civil society in designing a constructive net debate culture? And are there alternatives to foster an open-minded public discourse?

The steady progress in the fields of material science and processing technologies has made multi-layered radar absorbing structures (RAS) an attractive option w.r.t. stealth technologies. They possess the ability to reduce radar cross-section with minimum thickness and is therefore most preferred in airborne applications. As far as their electromagnetic performance is concerned, the sequence of material layers and thickness profile plays a pivotal role. Optimization of these two factors becomes complex in case of availability of large number of potential materials. Commonly used EM simulation software can be employed for the optimization of thickness profile. However, selection of suitable material layer sequence is out of their scope. In this context, a particle swarm optimization (PSO) based algorithm is presented for sequencing of material layers and optimization of thickness profile of multi-layered RAS configurations. The fitness function has been appropriately formulated to achieve maximum power absorption over broad band of frequencies and wide range of incident angles. Further, the efficacy of the algorithm has been demonstrated using a suitable case study.

Controlling a swarm of bee-like nanorobots in search of a target in a human body-like environment is a challenge. The challenge lies in the causal factors of emergence. This research focused on designing and developing a bee-inspired algorithm to provide instructions to coordinate simple and naïve nanorobots in search of a target. Experiments were conducted through simulations to determine the rate at which myriads of agents converge at a target. A maze environment depicting human blood vessels was used to deploy the nanorobots. Different sample population sizes were used, and the results indicate that the bigger the sample size, the higher the convergence speed. Compared with a single complicated robot, the swarm of intelligently controlled nanorobots proved efficient and effective in finding a target in a human-like environment.

Este artículo critica el deferencialismo científico. Este está basado en una errónea comprensión de la ciencia, pero ha fundamentado las críticas actuales a la democracia como proceso de toma de decisiones colectivas. Platón, uno de los primeros en ver la democracia como problema, pensó que la ignorancia e irracionalidad de la mayoría justificaba el gobierno de los sabios. Brennan, en Against Democracy (2016), ha defendido lo mismo. El autor ofrece, por el contrario, una alternativa innovadora basada en una concepción falibilista del conocimiento y la democracia, según la cual la autoridad y legitimidad de la democracia no dependen de su tendencia a tomar soluciones acertadas, sino de la tendencia de las multitudes inteligentes en la era de la información a evitar decisiones equivocadas.

This article examines some of the major mechanisms that might contribute to the apparent collective intelligence of the honey bee swarm and determines their role in its nest site decision making process. Decentralization of power allowed the swarm to utilize massively parallel information processing during its search for a new home. Various diversity generating mechanisms provided the swarm with a full range of potential nest sites for consideration. Numerous independent evaluations of nest site quality provided a relatively unbiased and noise free assessment of each site's survival worthiness. Differential net recruitment to the better quality sites aggregated evidence in favour of each site. A natural selection type process selected out the best competing sites and the first site to accumulate a quorum level of support was chosen as the swarm's new home. The article discusses the implications of these findings and how they relate to collective decision making by human groups. Copyright © 2016 John Wiley & Sons, Ltd.

Feature selection represents one of the critical processes in machine learning (ML). The fundamental aim of the problem of feature selection is to maintain performance accuracy while reducing the dimension of feature selection. Different approaches were created for classifying the datasets. In a range of optimization problems, swarming techniques produced better outcomes. At the same time, hybrid algorithms have gotten a lot of attention recently when it comes to solving optimization problems. As a result, this study provides a thorough assessment of the literature on feature selection problems using hybrid swarm algorithms that have been developed over time (2018-2021). Lastly, when compared with current feature selection procedures, the majority of hybrid algorithms enhance classification accuracy.