A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Nonlinear Characterization of the MRE Isolator Using Binary-Coded Discrete CSO and ELM

© 2018 World Scientific Publishing Company. Magnetorheological elastomer (MRE) isolator has been proved as a promising semi-active control device for structural vibration control. For its engineering application, developing an accurate and robust model is definitely necessary and also a challenging task. Most of the present models, belonging to parametric models, need to identify various model parameters and sometimes are not capable of perfectly capturing the unique characteristics of the device. In this work, a novel nonparametric model is proposed to characterize the inherent dynamics of the MRE isolator with the features of hysteresis and nonlinearity. Initially, dynamic tests are conducted to evaluate the performance of the isolator under various loading conditions, including harmonic, random, and seismic excitations. Then, on the basis of the captured experimental results, a hybrid learning method is designed to forecast the nonlinear responses of the device with known external inputs. In this method, a type of single hidden layer feed-forward network, called extreme learning machine (ELM), is developed to forecast the nonlinear responses (shear force) of the device with captured velocity, displacement, and current level. To obtain optimal performance of the developed model, an improved binary-coded discrete cat swarm optimization (BCDCSO) method is adopted to select optimal inputs and neuron number in the hidden layer for the network development. The performance of the proposed method is verified through the comparison between experimental results and model predictions. Due to the noise influence in the practical condition, the robustness of the proposed method is also validated via adding noise disturbance into the supplying currents. The results show that the proposed method outperforms the standard ELM in terms of characterization of the MRE isolator, even though the captured responses are polluted with external measurement noises

A novel optimized conical antenna array structure for back lobe cancellation of uniform concentric circular antenna arrays

In wireless communication systems, the existence of the antenna array back lobe represents a significant source of interference, which causes degradation of the signal-to-interference ratio (SIR), and power loss. In this paper, a novel optimized conical antenna array (O-CONAA) structure is proposed for back lobe cancellation of concentric circular antenna arrays (CCAA). Based on the CAA, It is considered to be made up Of several concentric circular antenna arrays (CCAA) which are placed in the X-Y plane. Firstly a non-optimized CONAA is constructed, by arranging these concentric CAAs with uniform vertical spacing along the Z-axis. Consequently, the CONAA seems to be treated as a combination between uniform CAAs and a linear antenna array (LAA). It has been noted that the CONAA radiation pattern has a back lobe amplitude the same as the main beam amplitude. The O-CONAA structure is suggested as a solution to this problem, which provides back lobe cancellation while maintaining the CONAA pattern characteristics like half power beamwidth (HPBW) side lobe level (SLL). The genetic algorithm(GA) approach is used in the O-CONAA structure to optimize the values of both CONAA inter-element spacing around the perimeter of each circle, and vertical spacing along the Z-axis to generate the desired radiation pattern

A novel optimized conical antenna array structure for back lobe cancellation of uniform concentric circular antenna arrays

In wireless communication systems, the existence of the antenna array back lobe represents a significant source of interference, which causes degradation of the signal-to-interference ratio (SIR), and power loss. In this paper, a novel optimized conical antenna array (O-CONAA) structure is proposed for back lobe cancellation of concentric circular antenna arrays (CCAA). Based on the CAA, It is considered to be made up Of several concentric circular antenna arrays (CCAA) which are placed in the X-Y plane. Firstly a non-optimized CONAA is constructed, by arranging these concentric CAAs with uniform vertical spacing along the Z-axis. Consequently, the CONAA seems to be treated as a combination between uniform CAAs and a linear antenna array (LAA). It has been noted that the CONAA radiation pattern has a back lobe amplitude the same as the main beam amplitude. The O-CONAA structure is suggested as a solution to this problem, which provides back lobe cancellation while maintaining the CONAA pattern characteristics like half power beamwidth (HPBW) side lobe level (SLL). The genetic algorithm(GA) approach is used in the O-CONAA structure to optimize the values of both CONAA inter-element spacing around the perimeter of each circle, and vertical spacing along the Z-axis to generate the desired radiation pattern

Branching strategies for mixed-integer programs containing logical constraints and decomposable structure

Decision-making optimisation problems can include discrete selections, e.g. selecting a route, arranging non-overlapping items or designing a network of items. Branch-and-bound (B&B), a widely applied divide-and-conquer framework, often solves such problems by considering a continuous approximation, e.g. replacing discrete variable domains by a continuous superset. Such approximations weaken the logical relations, e.g. for discrete variables corresponding to Boolean variables. Branching in B&B reintroduces logical relations by dividing the search space. This thesis studies designing B&B branching strategies, i.e. how to divide the search space, for optimisation problems that contain both a logical and a continuous structure. We begin our study with a large-scale, industrially-relevant optimisation problem where the objective consists of machine-learnt gradient-boosted trees (GBTs) and convex penalty functions. GBT functions contain if-then queries which introduces a logical structure to this problem. We propose decomposition-based rigorous bounding strategies and an iterative heuristic that can be embedded into a B&B algorithm. We approach branching with two strategies: a pseudocost initialisation and strong branching that target the structure of GBT and convex penalty aspects of the optimisation objective, respectively. Computational tests show that our B&B approach outperforms state-of-the-art solvers in deriving rigorous bounds on optimality. Our second project investigates how satisfiability modulo theories (SMT) derived unsatisfiable cores may be utilised in a B&B context. Unsatisfiable cores are subsets of constraints that explain an infeasible result. We study two-dimensional bin packing (2BP) and develop a B&B algorithm that branches on SMT unsatisfiable cores. We use the unsatisfiable cores to derive cuts that break 2BP symmetries. Computational results show that our B&B algorithm solves 20% more instances when compared with commercial solvers on the tested instances. Finally, we study convex generalized disjunctive programming (GDP), a framework that supports logical variables and operators. Convex GDP includes disjunctions of mathematical constraints, which motivate branching by partitioning the disjunctions. We investigate separation by branching, i.e. eliminating solutions that prevent rigorous bound improvement, and propose a greedy algorithm for building the branches. We propose three scoring methods for selecting the next branching disjunction. We also analyse how to leverage infeasibility to expedite the B&B search. Computational results show that our scoring methods can reduce the number of explored B&B nodes by an order of magnitude when compared with scoring methods proposed in literature. Our infeasibility analysis further reduces the number of explored nodes.Open Acces

Path Planning Optimization for Agricultural Spraying Robots Using Hybrid Dragonfly – Cuckoo Search Algorithm

Finding collision-free paths and optimized path coverage over an agricultural landscape has been a critical research problem among scientists and researchers over the years. Key precision farming strategies such as seeding, spraying fertilizers, and harvesting require special path planning techniques for efficient operations and will directly influence reducing the running cost of the farm. The main objective of this research work is to generate an optimized sequential route in an agricultural landscape with the nominal distance. In this proposed work, a novel Hybrid Dragonfly – Cuckoo Search algorithm is proposed and implemented to generate the sequential route for achieving spraying applications in greenhouse environments. Here the agricultural routing problem is expressed as a Travelling Salesman Problem, and the simulations are performed to find the effectiveness of the proposed algorithm. The proposed algorithm has generated better results when compared with other computational techniques such as PSO in terms of both solution quality and computational efficiency

Signal Perceptron: On the Identifiability of Boolean Function Spaces and Beyond

In a seminal book, Minsky and Papert define the perceptron as a limited implementation of what they called “parallel machines.” They showed that some binary Boolean functions including XOR are not definable in a single layer perceptron due to its limited capacity to learn only linearly separable functions. In this work, we propose a new more powerful implementation of such parallel machines. This new mathematical tool is defined using analytic sinusoids—instead of linear combinations—to form an analytic signal representation of the function that we want to learn. We show that this re-formulated parallel mechanism can learn, with a single layer, any non-linear k-ary Boolean function. Finally, to provide an example of its practical applications, we show that it outperforms the single hidden layer multilayer perceptron in both Boolean function learning and image classification tasks, while also being faster and requiring fewer parameters