Self-adaptive global best harmony search algorithm for training neural networks

AbstractThis paper addresses the application of Self-adaptive Global Best Harmony Search (SGHS) algorithm for the supervised training of feed-forward neural networks (NNs). A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on two classification benchmarking problems. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm is compared with other harmony search algorithms and the standard back-propagation algorithm. The experiments presented that the proposed algorithm lends itself very well to training of NNs and it is also highly competitive with the compared methods

Bending Response of Lattice Structure Filled Tubes under Transverse Loading

Thin-walled tubes are widely used as passive energy-absorbing structures in a variety of industries. These structures are typically filled with lightweight materials to improve their energy absorption capabilities. At this point, additive manufacturing technology offers a great chance researchers for the production of novel filler structures to increase the crashworthiness performance of thin-walled tubes. In the current work, additive manufacturable body-centered cubic (BCC) lattice structures are suggested as filling materials for thin-walled tubes, and the bending response of these structures is investigated under transverse loads via a finite element modeling approach. The aspect ratio and strut diameter are considered as design parameters, and three-point bending simulations are conducted to understand the transverse load bearing behaviors of the structures. Different loading offsets are also taken into account for three-point bending simulations. The numerical results revealed that the BCC lattice structures used as filler materials significantly increase the energy absorption performance of thin-walled tubes due to synergetic interactions. In particular, the simulation results revealed that the hybrid tubes can absorb up to 84% more energy than the empty tubes, while the crush force efficiency of these structures is up to 42% higher compared to the empty tubes. The present study also showed that the transverse crushing characteristics of tubes can be considerably improved by suitable selection of the design parameters. These primary outcomes reveal that the proposed lattice structures can be considered as a potential alternative to traditional filler materials for enhancing the bending response of thin-walled tubes under transverse loading

Failure Analysis of Graphene Sheets with Multiple Stone-Thrower-Wales Defects Using Molecular-Mechanics Based Nonlinear Finite Element Models

Experimental studies show that Stone-Thrower-Wales STW defects generally exist in graphene sheets GSs and these defects considerably affect the fracture strength of GSs. Thus, prediction of failure modes of GSs with STW defects is useful for design of graphene based nanomaterials. In this paper, effects of multiple STW defects on fracture behavior of GSs are investigated by employing molecular mechanics based nonlinear finite element models. The modified Morse potential is used to define the non-linear characteristic of covalent bonds between carbon atoms and geometric nonlinearity effects are considered in models. Different tilting angles of STW defects are considered in simulations. The analysis results showed that the fracture strength of GSs strongly depends on tilting angle of multiple STW defects and the STW defects cause significant strength loss in GSs. The crack initiation and propagation are also studied and brittle failure characteristics are observed for all samples. The results obtained from this study provide some insights into design of GS based-structures with multiple STW defects

A Tabu Search Based Approach for Graph Layout

This paper describes an automated tabu search based method for drawing general graph layouts with straight lines. To our knowledge, this is the first time tabu methods have been applied to graph drawing. We formulated the task as a multi-criteria optimization problem with a number of metrics which are used in a weighted fitness function to measure the aesthetic quality of the graph layout. The main goal of this work is to speed up the graph layout process without sacrificing layout quality. To achieve this, we use a tabu search based method that goes through a predefined number of iterations to minimize the value of the fitness function. Tabu search always chooses the best solution in the neighbourhood. This may lead to cycling, so a tabu list is used to store moves that are not permitted, meaning that the algorithm does not choose previous solutions for a set period of time. We evaluate the method according to the time spent to draw a graph and the quality of the drawn graphs. We give experimental results applied on random graphs and we provide statistical evidence that our method outperforms a fast search-based drawing method (hill climbing) in execution time while it produces comparably good graph layouts.We also demonstrate the method on real world graph datasets to show that we can reproduce similar results in a real world setting

A multi-agent framework for load consolidation in logistics

Logistics companies mainly provide land transportation services facing with difficulties in making effective operational decisions. This is especially the case of making load/capacity/route planning and load consolidation where customer orders are generally unpredictable and subject to sudden changes. Classical modelling and decision support systems are mostly insufficient for providing satisfactory solutions in a reasonable time solving such dynamic problems. Agent-based approaches, especially multi-agent paradigms that can be considered as relatively new members of system science and software engineering, are providing effective mechanisms for modelling dynamic systems generally operating under unpredictable environments and having a high degree of complex interactions. It seems that multi-agent paradigms have big potential for handling complex problems in land transportation logistics. Based on this motivation, the paper proposes a multi-agent based framework for load consolidation problems of third-party logistics companies

The multiple team formation problem using sociometry

The Team Formation problem (TFP) has become a well-known problem in the OR literature over the last few years. In this problem, the allocation of multiple individuals that match a required set of skills as a group must be chosen to maximise one or several social positive attributes. Specifically, the aim of the current research is two-fold. First, two new dimensions of the TFP are added by considering multiple projects and fractions of people's dedication. This new problem is named the Multiple Team Formation Problem (MTFP). Second, an optimization model consisting in a quadratic objective function, linear constraints and integer variables is proposed for the problem. The optimization model is solved by three algorithms: a Constraint Programming approach provided by a commercial solver, a Local Search heuristic and a Variable Neighbourhood Search metaheuristic. These three algorithms constitute the first attempt to solve the MTFP, being a variable neighbourhood local search metaheuristic the most efficient in almost all cases. Applications of this problem commonly appear in real-life situations, particularly with the current and ongoing development of social network analysis. Therefore, this work opens multiple paths for future research

An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem

The flexible job shop scheduling problem (FJSP) is vital to manufacturers especially in today’s constantly changing environment. It is a strongly NP-hard problem and therefore metaheuristics or heuristics are usually pursued to solve it. Most of the existing metaheuristics and heuristics, however, have low efficiency in convergence speed. To overcome this drawback, this paper develops an elitist quantum-inspired evolutionary algorithm. The algorithm aims to minimise the maximum completion time (makespan). It performs a global search with the quantum-inspired evolutionary algorithm and a local search with a method that is inspired by the motion mechanism of the electrons around an atomic nucleus. Three novel algorithms are proposed and their effect on the whole search is discussed. The elitist strategy is adopted to prevent the optimal solution from being destroyed during the evolutionary process. The results show that the proposed algorithm outperforms the best-known algorithms for FJSPs on most of the FJSP benchmarks

Deformation and energy absorption of additively manufactured functionally graded thickness thin-walled circular tubes under lateral crushing

This is an accepted manuscript of an article published by Elsevier in Engineering Structures on 10/10/2020, available online: https://doi.org/10.1016/j.engstruct.2020.111324 The accepted version of the publication may differ from the final published versionFunctionally graded thickness (FGT) is an innovative concept to create light-weight structures with better material distribution and promising energy absorption characteristics suitable for vehicle crashworthiness applications. Accordingly, this paper suggests innovative circular tubes with in-plane thickness gradient along their perimeter and assesses their crashworthiness behaviour under lateral loading. Three different designs of circular tubes with thickness gradient were considered in which the locations of maximum and minimum thicknesses are varied. Selective laser melting method of additive manufacturing was used to manufacture the different tubes. Two different bulk powders including titanium (Ti6Al4V) and aluminium (AlSi10Mg) were used in the manufacturing process. Quasi-static crush experiments were conducted on the laser melted tubes to investigate their crushing and energy absorption behaviour. The energy absorption characteristics of the different FGT tubes were calculated and compared against a uniform thickness design. The results revealed that the best crashworthiness metrics were offered by FGT titanium tube in which the maximum thickness regions were along the horizontal and vertical directions while the minimum thickness regions were at an angle of 45° with respect to the loading direction. The aforementioned tube was found to absorb 79% greater energy per unit mass than its uniform thickness counterpart. Finally, with the aid of numerical simulations and surrogate modelling techniques, multi-objective optimisation and parametric analysis were conducted on the best FGT tube. The influences of the geometrical parameters on the crashworthiness responses of the best FGT structure were explored and the optimal thickness gradient parameters were determined. The results reported in this paper provide valuable guidance on the design of FGT energy absorption tubes for lateral deformation.University of Wolverhampton early research award scheme (ERAS)Accepted versio

An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes

Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario present similarities in their fitness landscapes. That is, there are recurring features in the fitness landscapes when searching for solutions to different problem instances. We propose a methodology to exploit this characteristic by solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We use three goal-based objective functions and show that on benchmark instances of the multiobjective vehicle routing problem with time windows, the methodology is able to produce good results in short computation time. The methodology allows to combine the effectiveness of state-of-the-art multiobjective algorithms with the efficiency of goal programming to find good compromise solutions in problem scenarios where instances have similar fitness landscapes