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• AZ31 magnesium specimens were evaluated. • An exponential-trigonometric function model is constructed. • Artificial bee colony algorithm is used to optimize the weights of this function. • Estimated results are compared with the results of experimental studies. • Estimated results are used to plot Wöhler lines with very tight scatter bands. The fatigue life of magnesium alloy is influenced by many factors such as stress concentration factor, stress ratio and stress amplitude as well as different material states. Since all these factors affect the fatigue life of the alloy, the effects of these parameters need to be investigated. This study aims to obtain fatigue life values with satisfactory results on the samples of magnesium alloy AZ31. In order to solve the problem, firstly an exponential-trigonometric function model is constructed. Secondly, an artificial bee colony algorithm is used to optimize the weights of this function. Sample data of the alloy is used to verify the correctness of the fatigue life estimation model. The estimated results are compared with the results of experimental studies. Simulation results show that the fatigue life prediction model proposed in this paper can fit the experimental Wöhler lines with high estimation accuracy. The model based on artificial bee colony is a good candidate to estimate the fatigue life of magnesium alloy. [ABSTRACT FROM AUTHOR]

Highlights • A survey dedicated to mean-variance portfolio optimization is conducted. • A total of 175 papers published between 1998 and 2018 are selected and reviewed. • Deterministic models, including those with real-life constraints are studied. • The approaches are classified according to exact and inexact attempts in depth. Abstract Portfolio optimization is the process of determining the best combination of securities and proportions with the aim of having less risk and obtaining more profit in an investment. Utilizing covariance as a risk measure, mean-variance portfolio optimization model has brought a revolutionary approach to quantitative finance. Since then, along with the advancements in computational power and algorithmic enhancements, a lot of efforts have been made on improving this model by considering real-life conditions and solving model variants with various methodologies tested on various data and performance measures. A comprehensive literature review of recent and novel papers is crucial to establish a pattern of the past, and to pave the way on future directions. In this paper, a total of 175 papers published in the last two decades are selected within the scope of operations research community and reviewed in detail. Thus, a comprehensive survey on the deterministic models and applications suggested for mean-variance portfolio optimization in which several variants of this model as well as additional real-life constraints are studied. The review classifies the approaches according to exact and approximate attempts and analyzes the proposed algorithms based on various data and performance indicators in depth. Areas of future research are outlined. [ABSTRACT FROM AUTHOR]

One of the most studied variant of portfolio optimization problems is with cardinality constraints that transform classical mean–variance model from a convex quadratic programming problem into a mixed integer quadratic programming problem which brings the problem to the class of NP-Complete problems. Therefore, the computational complexity is significantly increased since cardinality constraints have a direct influence on the portfolio size. In order to overcome arising computational difficulties, for solving this problem, researchers have focused on investigating efficient solution algorithms such as metaheuristic algorithms since exact techniques may be inadequate to find an optimal solution in a reasonable time and are computationally ineffective when applied to large-scale problems. In this paper, our purpose is to present an efficient solution approach based on an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for solving cardinality constrained portfolio optimization problem. Computational results confirm the effectiveness of the solution methodology. [ABSTRACT FROM AUTHOR]

Along with the progress in computer hardware architecture and computational power, in order to overcome technological bottlenecks, software applications that make use of expert and intelligent systems must race against time where nanoseconds matter in the long-awaited future. This is possible with the integration of excellent solvers to software engineering methodologies that provide optimization-based decision support for planning. Since the logistics market is growing rapidly, the optimization of routing systems is of primary concern that motivates the use of vehicle routing problem (VRP) solvers as software components integrated as an optimization engine. A critical success factor of routing optimization is quality vs. response time performance. Less time-consuming and more efficient automated processes can be achieved by employing stronger solution algorithms. This study aims to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) which is a popular extension of the basic Vehicle Routing Problem arising in real world applications where pickup and delivery operations are simultaneously taken into account to satisfy the vehicle capacity constraint with the objective of total travelled distance minimization. Since the problem is known to be NP-hard, a hybrid metaheuristic algorithm based on an ant colony system (ACS) and a variable neighborhood search (VNS) is developed for its solution. VNS is a powerful optimization algorithm that provides intensive local search. However, it lacks a memory structure. This weakness can be minimized by utilizing long term memory structure of ACS and hence the overall performance of the algorithm can be boosted. In the proposed algorithm, instead of ants, VNS releases pheromones on the edges while ants provide a perturbation mechanism for the integrated algorithm using the pheromone information in order to explore search space further and jump from local optima. The performance of the proposed ACS empowered VNS algorithm is studied on well-known benchmarks test problems taken from the open literature of VRPSPD for comparison purposes. Numerical results confirm that the developed approach is robust and very efficient in terms of both solution quality and CPU time since better results provided in a shorter time on benchmark data sets is a good performance indicator. [ABSTRACT FROM AUTHOR]

Highlights: [•] This is the first adaptation of artificial bee colony algorithm to solve the SDDLBP. [•] The proposed algorithm is very easy to implement. [•] The algorithm was thoroughly tested on small and large sized instances. [•] Comparison with six other algorithms proves the superiority of the proposed method. [•] The algorithm provides near optimal solution for this NP-complete problem. [ABSTRACT FROM AUTHOR]