Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

A Modified Lagrange Method for Solving Convex Quadratic Optimization Problems

In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order derivative test for optimality of the Lagrangian function with respect to the primary variables of the problem, decomposes the solution process into two independent ones, in which the primary variables are solved for independently, and then the secondary variables, which are the Lagrange multipliers, are solved for, afterward. This is an innovation that leads to solving independently two simpler systems of equations involving the primary variables only, on one hand, and the secondary ones on the other. Solutions obtained for small sized problems (as preliminary test of the method) demonstrate that the new method is generally effective in producing the required solutions.

A New Multiobjective Particle Swarm Optimization Using Local Displacement and Local Guides

This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root distance mechanism into the external archives to enhance the diversity. We evaluate the performance of the proposed approach on a set of constrained and unconstrained multiobjective test functions, establishing a benchmark for comparison. In order to gauge its effectiveness relative to established techniques, we conduct a comprehensive comparison with well-known approaches such as SMPSO, NSGA2 and SPEA2. The numerical results demonstrate that our method not only achieves efficiency but also exhibits competitiveness when compared to evolutionary algorithms. Particularly noteworthy is its superior performance in terms of convergence and diversification, surpassing the capabilities of its predecessors.

A Comparative Study of Optimization Techniques on the Rosenbrock Function

In the evolving landscape of artificial intelligence and machine learning, the choice of optimization algorithm can significantly impact the success of model training and the accuracy of predictions. This paper embarks on a rigorous and comprehensive exploration of widely adopted optimization techniques, specifically focusing on their performance when applied to the notoriously challenging Rosenbrock function. As a benchmark problem known for its deceptive curvature and narrow valleys, the Rosenbrock function provides a fertile ground for examining the nuances and intricacies of algorithmic behavior. The study delves into a diverse array of optimization methods, including traditional Gradient Descent, its stochastic variant (SGD), and the more sophisticated Gradient Descent with Momentum. The investigation further extends to adaptive methods like RMSprop, AdaGrad, and the highly regarded Adam optimizer. By meticulously analyzing and visualizing the optimization paths, convergence rates, and gradient norms, this paper uncovers critical insights into the strengths and limitations of each technique. Our findings not only illuminate the intricate dynamics of these algorithms but also offer actionable guidance for their deployment in complex, real-world optimization problems. This comparative analysis promises to intrigue and inspire researchers and practitioners alike, as it reveals the subtle yet profound impacts of algorithmic choices in the quest for optimization excellence.

A New Filled Function with One Parameter to Solve Global Optimization

In this paper, a new filled function with only one parameter is proposed. The main advantages of the new filled function are that it not only can be analyzed easily, but also can be approximated uniformly by a continuously differentiable function. Thus, a minimizer of the proposed filled function can be obtained easily by using a local optimization algorithm. The obtained minimizer is taken as the initial point to minimize the objective function and a better minimizer will be found. By repeating the above processes, we will find a global minimizer at last. The results of numerical experiments show that the new proposed filled function method is effective.

Solution of Matrix Game with Triangular Intuitionistic Fuzzy Pay-Off Using Score Function

Using score function in a matrix game is very rare. In the proposed paper we have considered a matrix game with pay-off as triangular intuitionistic fuzzy number and a new ranking order has been proposed using value judgement index, available definitions and operations. A new concept of score function has been developed to defuzzify the pay-off matrix and solution of the matrix game has been obtained. A numerical example has been given in support of the proposed method.

Towards Optimizing a Personal Cooling Garment for Hot and Humid Deep Mining Conditions

Workers exposed to hot and humid conditions suffer from heat stress that affects their concentration and can potentially lead to an increase in workplace accidents. To minimize heat stress, protective equipment may be worn, such as personal cooling garments. This paper presents and discusses the performances, advantages and disadvantages of existing personal cooling garments, namely air-cooled, liquid-cooled, phase change, hybrid, gas expansion and vacuum desiccant cooling garments, and a thermoelectric cooling technology. The main objective is to identify the cooling technique that would be most suitable for deep mining workers. It appears that no cooling technology currently on the market is perfectly compatible with this type of mining environment. However, combining two or more cooling technologies into a single hybrid system could be the solution to an optimized cooling garment for deep mines.

Intermodal Freight Network Design for Transport of Perishable Products

The amount of perishable products transported via the existing intermodal freight networks has significantly increased over the last years. Perishable products tend to decay due to a wide range of external factors. Supply chain operations mismanagement causes waste of substantial volumes of perishable products every year. The heretofore proposed mathematical models optimize certain supply chain processes and reduce decay of perishable products, but primarily deal with local production, inventory, distribution, and retailing of perishable products. However, significant quantities of perishable products are delivered from different continents, which shall increase the total transportation time and decay potential of perishable products as compared to local deliveries. This paper proposes a novel optimization model to design the intermodal freight network for both local and long-haul deliveries of perishable products. The objective of the model aims to minimize the total cost associated with transportation and decay of perishable products. A set of piecewise approximations are applied to linearize the non-linear decay function for each perishable product type. CPLEX is used to solve the problem. Comprehensive numerical experiments are conducted using the intermodal freight network for import of the seafood perishable products to the United States to draw important managerial insights. Results demonstrate that increasing product decay cost may significantly change the design of intermodal freight network for transport of perishable products, cause modal shifts and affect the total transportation time and associated costs.

On the Quadratic Transportation Problem

We present a direct analytical algorithm for solving transportation problems with quadratic function cost coefficients. The algorithm uses the concept of absolute points developed by the authors in earlier works. The versatility of the proposed algorithm is evidenced by the fact that quadratic functions are often used as approximations for other functions, as in, for example, regression analysis. As compared with the earlier international methods for quadratic transportation problem (QTP) which are based on the Lagrangian relaxation approach, the proposed algorithm helps to understand the structure of the QTP better and can guide in managerial decisions. We present a numerical example to illustrate the application of the proposed method.

An Epsilon Half Normal Slash Distribution and Its Applications to Nonnegative Measurements

We introduce a new class of the slash distribution using the epsilon half normal distribution. The newly defined model extends the slashed half normal distribution and has more kurtosis than the ordinary half normal distribution. We study the characterization and properties including moments and some measures based on moments of this distribution. A simulation is conducted to investigate asymptotically the bias properties of the estimators for the parameters. We illustrate its use on a real data set by using maximum likelihood estimation.

A Smoothing Penalty Function Method for the Constrained Optimization Problem

In this paper, an approximate smoothing approach to the non-differentiable exact penalty function is proposed for the constrained optimization problem. A simple smoothed penalty algorithm is given, and its convergence is discussed. A practical algorithm to compute approximate optimal solution is given as well as computational experiments to demonstrate its efficiency.

Multi-Objective Optimization of Low Impact Development Designs in an Urbanizing Watershed

Multi-objective optimization linked with an urban stormwater model is used in this study to identify cost-effective low impact development (LID) implementation designs for small urbanizing watersheds. The epsilon-Non-Dominated Sorting Genetic Algorithm II (ε-NSGAII) has been coupled with the US Environmental Protection Agency’s Stormwater Management Model (SWMM) to balance the costs and the hydrologic benefits of candidate LID solutions. Our objective in this study is to identify the near-optimal tradeoff between the total LID costs and the total watershed runoff volume constrained by pre-development peak flow rates. This study contributes a detailed analysis of the costs and benefits associated with the use of green roofs, porous pavement, and bioretention basins within a small urbanizing watershed inState College,Pennsylvania. Beyond multi-objective analysis, this paper also contributes improved SWMM representations of LID alternatives and demonstrates their usefulness for screening alternative site layouts for LID technologies.

Boundary Control Problems for 2 ×2 Cooperative Hyperbolic Systems with Infinite Order Operators

In this study, boundary control problems with Neumann conditions for 2 × 2 cooperative hyperbolic systems involving infinite order operators are considered. The existence and uniqueness of the states of these systems are proved, and the formulation of the control problem for different observation functions is discussed.

Solving Ordinary Differential Equations with Evolutionary Algorithms

In this paper, the authors show that the general linear second order ordinary Differential Equation can be formulated as an optimization problem and that evolutionary algorithms for solving optimization problems can also be adapted for solving the formulated problem. The authors propose a polynomial based scheme for achieving the above objectives. The coefficients of the proposed scheme are approximated by an evolutionary algorithm known as Differential Evolution (DE). Numerical examples with good results show the accuracy of the proposed method compared with some existing methods.

Variable Fidelity Surrogate Assisted Optimization Using A Suite of Low Fidelity Solvers

Variable-fidelity optimization (VFO) has emerged as an attractive method of performing, both, high-speed and high-fidelity optimization. VFO uses computationally inexpensive low-fidelity models, complemented by a surrogate to account for the difference between the high-and low-fidelity models, to obtain the optimum of the function efficiently and accurately. To be effective, however, it is of prime importance that the low fidelity model be selected prudently. This paper outlines the requirements for selecting the low fidelity model and shows pitfalls in case the wrong model is chosen. It then presents an efficient VFO framework and demonstrates it by performing transonic airfoil drag optimization at constant lift, subject to thickness constraints, using several low fidelity solvers. The method is found to be efficient and capable of finding the optimum that closely agrees with the results of high-fidelity optimization alone.

On the Stable Method Computing Values of Unbounded Operators

Unbounded operators can transform arbitrarily small vectors into arbitrarily large vectors—a phenomenon known as instability. Stabilization methods strive to approximate a value of an unbounded operator by applying a family of bounded operators to rough approximate data that do not necessarily lie within the domain of unbounded operator. In this paper we shall be concerned with the stable method of computing values of unbounded operators having perturbations and the stability is established for this method.

Effects of Reneging, Server Breakdowns and Vacation on a Batch Arrival Single Server Queueing System with Three Fluctuating Modes of Service

This article examines the effects of reneging, server breakdown and server vacation on the various states of the batch arrivals queueing system with single server providing service to customers in three fluctuating modes. In this queueing system, any batch arrival joins the queue if the server is busy or on vacation or under repair. However, if the server is free, one customer from the arriving batch joins the service immediately while others join the queue. In case of server breakdown, the customer whose service is interrupted returns back to the head of the queue. As soon as the server has is repaired, the server attends to the customer in mode 1. For this queueing system, customers that are impatient due to breakdown and server vacation may renege (leave the queue without getting service). Due to fluctuating modes of service delivery, the system may provide service with complete or reduced efficiency. Consequently, we construct the mathematical model and derive the probability generating functions of the steady state probabilities of several states of the system including the steady state queue size distribution. Further, we discuss some particular cases of the proposed queueing model. We present numerical examples in order to demonstrate the effects of server vacation and reneging on the various states of the system. The study revealed that an increase in reneging and a decrease in server vacation results in a decrease in server utilization and an increase in server’s idle time provided rates of server breakdown and repair completion are constant. In addition, the probability of server vacation, the probability of system is under repair and the probabilities that the server provides service in three fluctuating modes decreases due to an increase in reneging and a decrease in vacation completion rates.

An Effective Approach to Determine an Initial Basic Feasible Solution: A TOCM-MEDM Approach

Transportation of products from sources to destinations with minimal total cost plays an important role in logistics and supply chain management. In this article, a new and effective algorithm is introduced for finding an initial basic feasible solution of a balanced transportation problem. Number of numerical illustration is introduced and optimality of the result is also checked. Comparison of findings obtained by the new heuristic and the existing heuristics show that the method presented herein gives a better result.

Two-Dimensional Topology Optimization of a Horn Antenna

A two-dimensional horn antenna is used as a model for topology optimization. In order to employ the topology optimization, each point in the domain is controlled by a function which is allowed to take values between 0 and 1. Each point’s distinct value then gives it an effective permittivity, either close to that of polyimide or that of air, two materials considered in this study. With these settings, the optimization problem becomes finding the optimal distribution of materials in a given domain, and is solved under constraints of reflection and material usage by the Method of Moving Asymptotes. The final configuration consists of two concentric arcs of air while polyimide takes up the rest of the domain, a result relatively unsensitive to the choice of constraints and initial values. Compared to the unoptimized antenna, a slimmer main lobe is observed and the gain boosts.

Model for Anticipating Failures by Omission in Calculation Grids

Computer grids are infrastructures in which heterogeneous and distributed resources offer very high computing or storage performance. If they offer extreme computing performance, they are also subject to the appearance of many failures related to this type of architecture. While performing tasks, if the response time of a node in the system incomprehensibly exceeds the requirements of the specifications, the node experiences an omission failure. The task running in the failed node will be unavailable until the node resumes normal activity. Waiting not being a possible solution, many fault tolerance methods have been proposed. Despite this large number of fault tolerance methods on offer, computer grids are still prone to many failures by omission. In this work, a numerical study of the failures by omission which occur in the calculation grids during the execution of the tasks was carried out and a model allowing anticipating its failures was proposed with the formalism PDEVS (Parallel Discret EVent system Specification).