A swarm is a group of a single species in which the members interact with one another and with the immediate environment without a principle for control or the emergence of a global intriguing ...behavior. Swarm-based metaheuristics, including nature-inspired populace-based methods, have been developed to aid the creation of quick, robust, and low-cost solutions for complex problems. Swarm intelligence was proposed as a computational modeling of swarms and has been successfully applied to numerous optimization tasks since its introduction. A correlation with the fundamental Particle Swarm Optimization (PSO) and PSO modifications demonstrates that hybrid swarm optimization outperforms existing strategies. The downside of hybrid swarm optimization is that it frequently tends to arrive at suboptimal solutions. As such, efforts are being made into combining HSO and other algorithms to arrive at better quality solutions.
Individual cells have numerous competencies in physiological and metabolic spaces. However, multicellular collectives can reliably navigate anatomical morphospace towards much larger, reliable ...endpoints. Understanding the robustness and control properties of this process is critical for evolutionary developmental biology, bioengineering, and regenerative medicine. One mechanism that has been proposed for enabling individual cells to coordinate toward specific morphological outcomes is the sharing of stress (where stress is a physiological parameter that reflects the current amount of error in the context of a homeostatic loop). Here, we construct and analyze a multiscale agent-based model of morphogenesis in which we quantitatively examine the impact of stress sharing on the ability to reach target morphology. We found that stress sharing improves the morphogenetic efficiency of multicellular collectives; populations with stress sharing reached anatomical targets faster. Moreover, stress sharing influenced the future fate of distant cells in the multi-cellular collective, enhancing cells’ movement and their radius of influence, consistent with the hypothesis that stress sharing works to increase cohesiveness of collectives. During development, anatomical goal states could not be inferred from observation of stress states, revealing the limitations of knowledge of goals by an extern observer outside the system itself. Taken together, our analyses support an important role for stress sharing in natural and engineered systems that seek robust large-scale behaviors to emerge from the activity of their competent components.
In this paper, a novel swarm-based metaheuristic algorithm inspired from the behaviors of beluga whales, called beluga whale optimization (BWO), is presented to solve optimization problem. Three ...phases of exploration, exploitation and whale fall are established in BWO, corresponding to the behaviors of pair swim, prey, and whale fall, respectively. The balance factor and probability of whale fall in BWO are self-adaptive which play significant roles to control the ability of exploration and exploitation. Besides, the Levy flight is introduced to enhance the global convergence in the exploitation phase. The effectiveness of the proposed BWO is tested using 30 benchmark functions, with qualitative, quantitative and scalability analysis, and the statistical results are compared with 15 other metaheuristic algorithms. According to the results and discussion, BWO is a competitive algorithm in solving unimodal and multimodal optimization problems, and the overall rank of BWO is the first in the scalability analysis of benchmark functions among compared metaheuristic algorithms through the Friedman ranking test. Finally, four engineering problems demonstrate the merits and potential of BWO in solving complex real-world optimization problems. The source code of BWO is currently available to public: https://ww2.mathworks.cn/matlabcentral/fileexchange/112830-beluga-whale-optimization-bwo/.
•A novel metaheuristic algorithm named as Beluga Whale Optimization (BWO) is proposed.•The behaviors of swim, prey and whale fall are designed on BWO algorithm.•The BWO is tested on 30 well-known benchmark functions and 4 engineering problems.•The BWO is compared with 15 well-known metaheuristic algorithms.•The BWO outperforms comparing algorithms in benchmark functions, especially for scalability of dimension.
In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO). The main inspiration of HHO is the cooperative behavior and ...chasing style of Harris’ hawks in nature called surprise pounce. In this intelligent strategy, several hawks cooperatively pounce a prey from different directions in an attempt to surprise it. Harris hawks can reveal a variety of chasing patterns based on the dynamic nature of scenarios and escaping patterns of the prey. This work mathematically mimics such dynamic patterns and behaviors to develop an optimization algorithm. The effectiveness of the proposed HHO optimizer is checked, through a comparison with other nature-inspired techniques, on 29 benchmark problems and several real-world engineering problems. The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques. Source codes of HHO are publicly available at http://www.alimirjalili.com/HHO.html and http://www.evo-ml.com/2019/03/02/hho.
•A mathematical model is proposed to simulate the hunting behavior of Harris’ Hawks.•An optimization algorithm is proposed using the mathematical model.•The proposed HHO algorithm is tested on several benchmarks.•The performance of HHO is also examined on several engineering design problems.•The results show the merits of the HHO algorithm as compared to the existing algorithms.
•An artificial Jellyfish Search (JS) optimizer inspired by jellyfish behavior is proposed.•JS has only two control parameters, which are population size and number of iterations.•The new algorithm is ...successfully tested on benchmark functions and optimization problems.•JS optimizer outperforms well-known metaheuristic algorithms and prior studies.
This study develops a novel metaheuristic algorithm that is motivated by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer. The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom. JS optimizer is tested using a comprehensive set of mathematical benchmark functions and applied to a series of structural engineering problems. Fifty small/average-scale and twenty-five large-scale functions involving various dimensions were used to validate JS optimizer, which was compared with ten well-known metaheuristic algorithms. JS optimizer was found to outperform those algorithms in solving mathematical benchmark functions. The JS algorithm was then used to solve structural optimization problems, including 25-bar tower design, 52-bar tower design and 582-bar tower design problems. In those cases, JS not only performed best but also required the fewest evaluations of objective functions. Therefore, JS is potentially an excellent metaheuristic algorithm for solving optimization problems.
The Sustainable Development Goals (SDGs) present the emerging need to explore new ways of AgriFood production and food security as ultimate targets for feeding future generations. The study adopts a ...Design Science methodology and proposes Artificial Intelligence (AI) techniques as a solution to food security problems. Specifically, the proposed artefact presents the collective use of Agricultural Technology (AgriTech) drones inspired by the biomimetic ways of bird swarms. The design (artefact) appears here as a solution for supporting farming operations in inaccessible land, so as unmanned aerial devices contribute and improve the productivity of farming areas with limited capacity. The proposed design is developed through a scenario of drone swarms applying AI techniques to address food security issues. The study concludes by presenting a research agenda and the sectoral challenges triggered by the applications of AI in Agriculture.
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