MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO’s premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally.

A Self-adaptive Hybrid Bio-inspired Optimization Algorithm by Using Sigmoidal Function

The article presents a new hybrid algorithm, which designs based on traditional bio-inspired optimization algorithms. The algorithm leverages the advantage of Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC), replacing other algorithm weaknesses. A new algorithm we proposed is the Fast bio-inspired Optimization Algorithm (FOA). The DE uses multi-parent for trial vector calculation. It increases the diversity of the solution, while the sigmoidal function adds a self-adaptive characteristic to the proposed algorithm. The function replaces a weighting scheme of PSO. In sub-optimal avoidance, the FOA includes a scout bee behavior from ABC. It makes FOA providing the solution faster than traditional versions, while the solution quality is maintained at an acceptable level. According to a new design, an FOA can reduce the algorithm runtime up to 43.57%, 37.14%, 40.78%, and 31.30% compared to PSO, DE, ABC, and DEPSO, respectively. The DEPSO is the hybrid algorithm between DE and PSO. The best solution to FOA is better than the traditional version of the algorithms. The new algorithm design and the optimization speed improvement are the highlight contribution of this article.

Efficient Virtual Tribomechadynamics by Means of Joint Modes for Detailed Investigation of Complex Local Stick and Slip Behavior Inside a Joint

In the last years, the numerical and experimental research effort on joint nonlinearities and tribomechadynamics has increased. Thereby, local sticking and slipping effects as well as the influence of friction caused damping on the global dynamics are of interest. Conventional computational approaches like model order reduction techniques or the finite element method lead either to insufficient result quality or a high computational burden. For the efficient numerical consideration of jointed structures in combination with model order reduction, joint modes based on trial vector derivatives have been presented. These joint modes enable accurate computation of local nonlinear contact and friction forces together with efficient time integration even for high fidelity finite element models. This article describes the application of joint modes for efficient virtual tribomechadynamics. Therefore, a generic structure including a bolted joint is used. It is investigated if these joint modes reproduce local friction stress, and sticking/slipping areas comparable to the nonlinear finite element method within reasonable computational times. Moreover, global damping effects are studied at different preload levels and related to local sticking/slipping behavior. The numerical studies confirm that joint modes lead to accurate results with low computation effort and hence allow an efficient and detailed virtual investigation of complex joints. In addition, this publication shows that the consideration of tangential stiffness for the computation of joint modes remarkably increases the local result quality.

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

This research aims to solve the problem of the raw milk collection and transportation system which can be interpreted as a special case of the vehicle routing problem. In the proposed problem, the factory will send the trucks, multiple fleets composed of several compartments, to collect the raw milk from the raw milk farms. The objective of this research is to minimize the total transportation cost and the trucks’ and tanks’ cleaning costs. The transportation cost directly depends on the fuel usage. The fuel usage occurs during the transportation of the milk and during the waiting times when it arrives at the factory and cannot transfer the raw milk into the production tank. We develop the modified differential evolution algorithm (MDE) to solve the proposed problem. The original process of the Differential Evolution algorithm (DE) has been modified in two folds which are as follows: (1) In the recombination process, the 2nd order of trial vectors has been generated using 3 different strategies and compared with the 1st order trial vector; the better from the 1st and the 2nd order of trial vectors will move to the selection process. (2) The probability function has been used to select the new target vector from one of two sources which are the trial vector and the current target vector so that the worse solution can be accepted in order to increase the diversity of the original DE. The computational result shows that the modified DE (MDE) outperforms the original DE in finding a better solution.

Numerical Assessment of Reduced Order Modeling Techniques for Dynamic Analysis of Jointed Structures With Contact Nonlinearities

This work presents an assessment of classical and state of the art reduced order modeling (ROM) techniques to enhance the computational efficiency for dynamic analysis of jointed structures with local contact nonlinearities. These ROM methods include classical free interface method (Rubin method, MacNeal method), fixed interface method Craig-Bampton (CB), Dual Craig-Bampton (DCB) method and also recently developed joint interface mode (JIM) and trial vector derivative (TVD) approaches. A finite element (FE) jointed beam model is considered as the test case taking into account two different setups: one with a linearized spring joint and the other with a nonlinear macroslip contact friction joint. Using these ROM techniques, the accuracy of dynamic behaviors and their computational expense are compared separately. We also studied the effect of excitation levels, joint region size, and number of modes on the performance of these ROM methods.

A History-Driven Differential Evolution Algorithm for Optimization in Dynamic Environments

This paper presents a novel differential evolution algorithm to solve dynamic optimization problems. In the proposed algorithm, the entire population is composed of several subpopulations, which are evolved independently and excluded each other by a predefined Euclidian-distance. In each subpopulation, the “DE/best/1” mutation operator is employed to generate a mutant individual in this paper. In order to fully exploit the newly generated individual, the selection operator was extended, in which the newly generated trial vector competed with the worst individual if this trial vector was worse than the target vector in terms of the fitness. Meanwhile, this trial vector was stored as the historical information, if it was better than the worst individual. When the environmental change was detected, some of the stored solutions were retrieved and expected to guide the reinitialized solutions to track the new location of the global optimum as soon as possible. The proposed algorithm was compared with several state-of-the-art dynamic evolutionary algorithms over the representative benchmark instances. The experimental results show that the proposed algorithm outperforms the competitors.

Numerical Assessment of Reduced Order Modeling Techniques for Dynamic Analysis of Jointed Structures With Contact Nonlinearities

This work presents an assessment of classical and state of the art reduced order modelling (ROM) techniques to enhance the computational efficiency for dynamic analysis of jointed structures with local contact nonlinearities. These ROM methods include classical free interface method (Rubin method, MacNeal method), fixed interface method (Craig-Bampton), Dual Craig-Bampton (DCB) method and also recently developed joint interface mode (JIM) and trial vector derivative (TVD) approaches. A finite element jointed beam model is considered as the test case taking into account two different setups: one with a linearized spring joint and the other with a nonlinear macro-slip contact friction joint. Using these ROM techniques, the accuracy of dynamic behaviors and their computational expense are compared separately. We also studied the effect of excitation levels, joint region size and number of modes on the performance of these ROM methods.