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Purpose: The present paper aims at the multi-objective optimization of a reentrant hexagonal cell auxetic structure. In addition, a parametric analysis will be carried out to verify how each of the design factors impact each of the responses. Design/methodology/approach: The multi-objective optimization of five different responses of an auxetic model was considered: mass, critical buckling load under compression effort, natural frequency, Poisson's ratio and failure load. The response surface methodology was applied, and a new meta-heuristic of optimization called the multi-objective Lichtenberg algorithm was applied to find the optimized configuration of the model. It was possible to increase the failure load by 26.75% in compression performance optimization. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, the failure load by 28.70% and reduce the mass and Poisson's ratio by 15.97 and 11%, respectively. This paper addresses something new in the scientific world to date when evaluating in a multi-objective optimization problem, the compression and modal performance of an auxetic reentrant model. Findings: It was possible to find multi-objective optimized structures. It was possible to increase the critical buckling load by 42.82%, and the failure load in compression performance by 26.75%. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%, and decrease the mass by 15.97%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, increase the failure load by 28.70% and reduce the mass and Poisson's ratio by 15.97 and 11%, respectively. Originality/value: There is no work in the literature to date that performed the optimization of 5 responses simultaneously of a reentrant hexagonal cell auxetic structure. This paper also presents an unprecedented statistical analysis in the literature that verifies how the design factors impact each of the responses. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to offer a hybrid genetic algorithm and the ant colony optimization (GA-ACO) algorithm for task mapping and resource management. The paper aims to reduce the makespan and total response time in fog computing- medical cyber-physical system (FC-MCPS). Design/methodology/approach: Swift progress in today's medical technologies has resulted in a new kind of health-care tool and therapy techniques like the MCPS. The MCPS is a smart and reliable mechanism of entrenched clinical equipment applied to check and manage the patients' physiological condition. However, the extensive-delay connections among cloud data centers and medical devices are so problematic. FC has been introduced to handle these problems. It includes a group of near-user edge tools named fog points that are collaborating until executing the processing tasks, such as running applications, reducing the utilization of a momentous bulk of data and distributing the messages. Task mapping is a challenging problem for managing fog-based MCPS. As mapping is an non-deterministic pol ynomial-time-hard optimization issue, this paper has proposed a procedure depending on the hybrid GA-ACO to solve this problem in FC-MCPS. ACO and GA, that is applied in their standard formulation and combined as hybrid meta-heuristics to solve the problem. As such ACO-GA is a hybrid meta-heuristic using ACO as the main approach and GA as the local search. GA-ACO is a memetic algorithm using GA as the main approach and ACO as local search. Findings: MATLAB is used to simulate the proposed method and compare it to the ACO and MACO algorithms. The experimental results have validated the improvement in makespan, which makes the method a suitable one for use in medical and real-time systems. Research limitations/implications: The proposed method can achieve task mapping in FC-MCPS by attaining high efficiency, which is very significant in practice. Practical implications: The proposed approach can achieve the goal of task scheduling in FC-MCPS by attaining the highest total computational efficiency, which is very significant in practice. Originality/value: This research proposes a GA-ACO algorithm to solve the task mapping in FC-MCPS. It is the most significant originality of the paper. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present a new multi-community meta-heuristic optimization algorithm, which is called shuffled shepherd optimization algorithm (SSOA). In this algorithm. Design/methodology/approach: The agents are first separated into multi-communities and the optimization process is then performed mimicking the behavior of a shepherd in nature operating on each community. Findings: A new multi-community meta-heuristic optimization algorithm called a shuffled shepherd optimization algorithm is developed in this paper and applied to some attractive examples. Originality/value: A new metaheuristic is presented and tested with some classic benchmark problems and some attractive structures are optimized. [ABSTRACT FROM AUTHOR]