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For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

This paper introduces a new variant of the Bees Algorithm (BA) called Bees Algorithm with 2-parameter (BA 2 ), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA's parameters by combining exploration and exploitation strategies while preserving the algorithm's core principles to efficiently search for optimal solutions. The paper provides a detailed description of the algorithm's core principles and its application to two engineering problems, the air-cooling system design (ACSD) and the printed circuit board assembly sequence optimisation (PASO). The results show that BA 2 outperforms previous versions of the basic BA in terms of convergence speed and solution quality. However, the authors acknowledge that further research is needed to test the scalability and generalisability of the algorithm to larger and more diverse optimisation problems. Overall, this paper provides valuable insights into the potential of metaheuristics for solving real-world optimisation problems.

As electric vehicles (EVs) are currently under‐utilised, the features of deploying EVs as distributed energy resources (DERs), based on an EV as a service (EVaaS) framework, are exploited and a resource allocation scheme is proposed for optimum association of dispersed EVs with critical load for demand fulfilment in microgrids. The proposed approach is based on a combined economic emission (CEE) optimisation model where both energy costs and carbon emissions are taken into account. The CEE optimisation problem is then formulated as a bi‐objective optimisation problem, considering a number of practical constraints, such as energy demand, cost budget, emission limit and charging station limit. Carbon price is introduced to convert the bi‐objective problem into a single objective function. The authors included EV battery degradation cost to ensure EV owners are not worse off after EVaaS participation. The feasibility of the proposed model is demonstrated in simulation studies. The approach has been extended to evaluate the trade‐off between EVaaS and conventional DERs. Numerical results demonstrate the efficiency of the proposed resource allocation scheme.

In recent years, swarm intelligence metaheuristic algorithms have emerged as powerful tools for solving real-world engineering optimization problems. However, their performance often degrades when applied to complex, high-dimensional problems. To address this limitation, we propose an Adaptive Fuzzy Swarm-based Search Algorithm (AFSSA), which incorporates a Fuzzy Dynamic Control Mechanism to dynamically adjust the optimization coefficients of swarm intelligence algorithms. AFSSA employs a Mamdani fuzzy inference system to enable smooth phase transitions during optimization, ensuring adaptability to the problem's unique characteristics. In this study, AFSSA is applied to enhance the acceleration coefficients of Particle Swarm Optimization (PSO) and Golden Search Optimization (GSO), resulting in AFSSA-PSO and AFSSA-GSO. The performance of these modified algorithms is evaluated on 23 standard benchmark functions (with dimensions of 30, 100, and 500) and the CEC2019 test suite, showing competitive results compared to other well-known optimization methods. Additionally, AFSSA is tested on data clustering problems, further demonstrating its versatility in handling complex real-world applications.

In this paper, we consider a multiple objective optimization problem whose decision variables and parameters are intervals. Existence of solution of this problem is studied by parameterizing the intervals. A methodology is developed to find the tω-efficient solution of the problem. The original problem is transformed to an equivalent deterministic problem and the relation between solutions of both is established. Finally, the methodology is verified in numerical examples.

Control technique based on the well-known Takagi–Sugeno (T–S) models offers a powerful and systematic tool to cope with complex non-linear systems. This study presents a new method to design robust H∞ controllers stabilising the switching uncertain and disturbed T–S systems subject to control input saturation. To this end, the input saturation is taken into account in the control design under its polytopic form. The Lyapunov stability theory is used to derive the design conditions, which are formulated as a linear matrix inequality (LMI) optimisation problem. The controller design amounts to solving a set of LMI conditions with some numerical tools. In comparison with previous results, the proposed method not only provides a simple and efficient design procedure to deal with a large class of input saturated non-linear systems but also leads to less conservative design conditions. Moreover, with a simple shape criterion, the proposed approach maximises also the estimated domain of attraction included inside the validity domain of the system. The validity of the proposed method is illustrated through academic as well as real industrial examples.

Dynamic economic dispatch problem in power system considering valve-point effects of generators is a non-smooth, non-convex and multi-dimensional constrained optimisation problem. In allusion to those characteristics, this study proposes a hybrid algorithm which integrates low-discrepancy sequences, improved shuffled frog leaping algorithm and sequential quadratic programming. The effectiveness of the proposed method has been verified by using case studies based on 5-unit, 10-unit and 30-unit test systems over a period of 24 h. The results show that the proposed method has improved solution quality and computation efficiency, compared with most current approaches.

Jaya is a metaheuristic algorithm that uses a pair of random internal parameters to adjust its exploration and exploitation search behaviors. Such a random setting can negatively affect the search performance of the algorithm by causing inappropriate search behavior in some iterations. To tackle this issue, the present study deals with developing a new fuzzy decision-making mechanism for dynamic adjusting the trade-off between the exploration and exploitation search behaviors of the Jaya method. The new algorithm is named Fuzzy Reinforced Jaya (FRJ) method. The search capability of the FRJ is evaluated in solving a suite of unconstrained mathematical benchmarks and constrained mechanical and structural optimization problems with buckling and natural frequency constraints. Also, the relevant decision variables are selected from both continuous and discrete domains. To provide a deeper insight into the effect of the defined auxiliary fuzzy module, the performance of the algorithm is evaluated and discussed using normalized diversity concept and behavioral diagrams. Also, employing different statistical analyses (e.g., Q – Q diagrams, Wilcoxson and Friedman tests), the significance of the outcomes is evaluated. Also, the numeric achievements are compared with six other well-stablished techniques. Attained outcomes indicate that the proposed FRJ, as a self-adaptive and parameter-free method, provides superior and promising results in the terms of stability, accuracy, and computational cost in solving mathematical and structural optimization problems.

Particle swarm optimization (PSO) is a well-known population-based stochastic optimization algorithm intended by collective and communicative behavior of bird flocks looking for food. Being a very powerful tool for obtaining the global optimal solution, PSO has experienced a multitude of enhancements during the last three decades. The algorithm has been modified, hybridized and extended by various authors in terms of structural variations, parameters selection and tuning, convergence analysis and meta-heuristics. In this article, hybridized PSO has been proposed to solve balanced transportation problem, a discrete optimization problem, of any number of decision variables converging to the global optima. Two additional modules have been embedded within the PSO, in order to repair the negative and/or fractional values of the decision variables, and tested with variants of parameters present therein. The proposed algorithm generates an optimal solution even without considering the rigid conditions of the traditional techniques. The paper compares the performance of different variants of inertia weight, acceleration coefficients and also the population size with respect to the convergence to the optimal solution. The performance of the proposed algorithm is statistically validated using the paired t test.

In this study, the problem of determining the power allocation that maximises the energy efficiency of cognitive radio network is investigated as a constrained fractional programming problem. The energy-efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non-linear non-convex optimisation problem. The authors first transform the energy-efficient maximisation problem into a parametric optimisation problem and then propose an iterative power allocation algorithm that guarantees ε-optimal solution. A proof of convergence is also given for the ε-optimal algorithm. The proposed ε-optimal algorithm provide a practical solution for power allocation in energy-efficient cognitive radio networks. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithms are investigated.