Design Of Optimization Algorithms Research Articles

Selective-candidate framework with similarity selection rule for evolutionary optimization

Achieving better exploitation and exploration capabilities (EEC) have always been an important yet challenging issue in the design of evolutionary optimization algorithm (EOA). The difficulties lie in obtaining a good balance in EEC, which is determined cooperatively by operations and parameters in an EOA. When deficiencies in exploitation or exploration are observed, most existing works consider a piecemeal approach, either by designing new operations or by altering the parameters. Unfortunately, when different situations are encountered, these proposals may fail to be the winner. To address these problems, this paper proposes an explicit EEC control method named selective-candidate framework with similarity selection rule (SCSS). M (M ​> ​1) candidates are first generated from each current solution with independent operations and parameters to enrich the search. Then, a similarity selection rule is designed to determine the final candidate by considering the fitness ranking of the current solution and its Euclidian distance to each of these M candidates. Superior current solutions will prefer the closest candidates for efficient local exploitation while inferior ones will favor the farthest for exploration purpose. In this way, the rule could synthesize exploitation and exploration, making the evolution more effective. When applied to three classic, four state-of-the-art and four up-to-date EOAs from branches of differential evolution, evolution strategy and particle swarm optimization, significant enhancement in performance is achieved.

Robust and structure exploiting optimisation algorithms: an integral quadratic constraint approach

We consider the problem of analysing and designing gradient-based discrete-time optimisation algorithms for a class of unconstrained optimisation problems having strongly convex objective functions with Lipschitz continuous gradient. By formulating the problem as a robustness analysis problem and making use of a suitable adaptation of the theory of integral quadratic constraints, we establish a framework that allows to analyse convergence rates and robustness properties of existing algorithms and enables the design of novel robust optimisation algorithms with prespecified guarantees capable of exploiting additional structure in the objective function.

Multi-objective optimization model and algorithm design of road network in tourist town

To alleviate traffic congestion in tourist towns, we propose a method to optimize the road network design by combining the macro-traffic simulation software with the heuristic algorithm. Based on the characteristics of traffic flow in tourist towns, the problem of optimal design of tourism urban road network is transformed into a multi-objective optimization model. To seek the optimal balance point between traffic congestion management and road resource investment, a multi-objective optimization model is established to optimize the saturation system of each road section and minimize the investment cost. We use TransCAD simulation software and the user equilibrium model to distribute the traffic flow of the initial road network, then, design the model algorithm based on genetic algorithm, and at last, design Matlab algorithm program to solve the model. The optimal weight of the objective function is determined by multiple algorithm experiments and comparative analysis of the experimental results. The results of the numerical example show that the total saturation value of all sections in the road network decreases from 16.70 to 11.12, and the average saturation value of all sections in the road network decreases from 0.93 to 0.62. The saturation of optimized sections is stable in a reasonable range from 0.5 to 0.8. The overall road network saturation is significantly reduced and becomes more balanced, moreover, the cost of investment is more reasonable. Therefore, results of the study have certain guiding significance for the road network planning of new tourist towns.

Gradient-based Simulation Optimization using Approximated Systems

We propose gradient-based simulation optimization algorithms for complicated stochastic systems, for which the exact simulation of the objective function is extremely costly or impossible. For such complicated stochastic systems, a sequence of systems can often be constructed to approximate the original complicated stochastic system, which has finer and finer approximation resolution but higher and higher cost to simulate. With the goal of optimizing the original system, our proposed algorithms strategically and sequentially utilize the approximated systems to construct stochastic gradients and perform gradient search in the decision space. Various gradient construction methods are analyzed and can accommodate different features of the approximated systems such as discontinuity. As a theory support, we prove algorithm convergence, the convergence rate, central limit theorem, and optimal algorithm design under the assumption that the objective function for original system is strong convex, while no such assumptions on the approximated systems are required. Moreover, if the sequence of approximated systems can be constructed in a coupled way, we develop multi-level gradient-based optimization algorithms. We prove theoretically and then show empirically that the additional use of multi-level structure can further improve the convergence rate and computational efficiency of the proposed simulation optimization algorithms.

Real time evaluation algorithm of human motion in tennis training robot

Tennis is a set of sports and entertainment and a sports activity, since 2014, tennis in China has been another rapid development. With the development of economy and technology, tennis training mode has been further optimized and reformed. At present, tennis training robot is the mainstream way to train athletes. However, there are some defects in the current tennis training robots, such as the low accuracy of human motion real-time evaluation, and the lack of stability. Therefore, this paper puts forward the related research on the real-time evaluation algorithm of human motion in tennis training robots, hoping to make up for the deficiency in this field. The research of this paper is mainly divided into four parts. The first part is to analyze the current situation of technology research in this field and put forward the idea of this paper by analyzing the shortcomings of the existing technology. The second part is the related basic theory research; this part deeply studies the core theory of tennis training and intelligent training robot, which provides a theoretical basis for the realization of the optimization scheme. The third part is the design and implementation of a real-time human motion evaluation optimization algorithm for tennis training robots. At the end of the paper, that is, the fourth part, through the way of field test and investigation, further proves the superiority of the improved real-time evaluation algorithm of human movement. The algorithm has good stability and accuracy and can meet the existing tennis training requirements.

Optimization Model and Algorithm Design for Rural Leisure Tourism Passenger Flow Scheduling

A constrained optimization model and an iterative optimization algorithm based on PSO are designed for rural leisure tourism passenger flow scheduling. Compared with the traditional tourist dispatching scheme, this model maximizes the overall tourist experience and operation profit of the whole region on the base of protection of tourists' travel experience and the interests of operators in the dispatching spots. Simulations and comparisons are taken to evaluate the feasibility and effectiveness of the model and the optimization. The simulation results show that compared with the shortest-distance-based traffic scheduling scheme and the gravity-model-based scheme, the new model and optimization could meet the requirements of the rural leisure tourists dispatching and bring better tourist experience and tourism profit.

Slime mould foraging: an inspiration for algorithmic design

The metaphor of 'foraging as search' provides a rich source of inspiration for the design of optimisation algorithms. An extensive literature has resulted in computer science over the past twenty years based on this, with algorithmic families such as ant colony optimisation and honeybee optimisation amongst others, being successfully applied to a wide range of real-world problems. Of course, all organisms must forage to acquire necessary resources and in recent years, increasing attention has been paid to the mechanisms by which non-neuronal organisms, in other words organisms without a central nervous system, forage. The vast majority of living organisms, encompassing some 99.5% of all biomass on earth, are non-neuronal. In this paper we introduce the plasmodial slime mould Physarum polycephalum. This non-neuronal organism is formed when individual amoebae swarm together and fuse, resulting in a large bag of cytoplasm encased within a thin membrane which acts a single organism. Inspiration has been drawn from some of its foraging behaviours to develop algorithms for graph optimisation and exemplars of these algorithms along with some suggestions for future research are presented in this paper.

Slime mould foraging: an inspiration for algorithmic design

The metaphor of 'foraging as search' provides a rich source of inspiration for the design of optimisation algorithms. An extensive literature has resulted in computer science over the past twenty years based on this, with algorithmic families such as ant colony optimisation and honeybee optimisation amongst others, being successfully applied to a wide range of real-world problems. Of course, all organisms must forage to acquire necessary resources and in recent years, increasing attention has been paid to the mechanisms by which non-neuronal organisms, in other words organisms without a central nervous system, forage. The vast majority of living organisms, encompassing some 99.5% of all biomass on earth, are non-neuronal. In this paper we introduce the plasmodial slime mould Physarum polycephalum. This non-neuronal organism is formed when individual amoebae swarm together and fuse, resulting in a large bag of cytoplasm encased within a thin membrane which acts a single organism. Inspiration has been drawn from some of its foraging behaviours to develop algorithms for graph optimisation and exemplars of these algorithms along with some suggestions for future research are presented in this paper.

Fitness landscapes analysis and adaptive algorithms design for traffic lights optimization on SIALAC benchmark

Finding optimal traffic light timings at road intersections is a mandatory step for urban planners wishing to achieve a sustainable mobility in modern cities. Increasing congestion situations constantly require urbanists to enhance traffic fluidity, while limiting pollutant emissions and vehicle consumption to improve inhabitants’ welfare. Various mono or multi-objective optimization methods, such as evolutionary algorithms, fuzzy logic algorithms or even particle swarm optimizations, help to reach optimal traffic signal settings. However, those methods are usually designed to tackle very specific transportation configurations.Here, we introduce an extended version of the sialac benchmark, bringing together several real-world-like study cases with various features related to population, working activities, or traffic light devices. We drive a fitness landscape analysis on these various benchmark instances, which helps to improve the design of optimization algorithms for this class of real-world mobility problems. Thereby, we propose a new adaptive optimization algorithm to tackle each scenario of the benchmark.

The fuzzy particle swarm optimization algorithm design for dynamic positioning system under unexpected impacts

The vessel motion is a nonlinear and complicated in practical applications. The factors which affect vessel motion, mainly come from environmental influences. In this paper, we develop a fuzzy particle swarm optimization algorithm that applies to dynamic positioning system for stabilizing a vessel motion under unexpected impacts. The structure parameter of fuzzy system is calibrated by particle swarm optimization method. The coverage domain width and the overlap degree influence of membership function are adjusted dynamically from system errors. Thereby optimizing the control signal and enhancing the dynamic positioning system quality. Simulation studies with comparisons on a supply vessel are carried out. The proposed in a better response compared to other method such as fuzzy that proved effective of the proposed controller.

Symmetric >67 Gbps OFDM-IMDD based WDM access network for mitigating Rayleigh backscattering interference noise

In this paper, we propose a symmetric >67 Gbps/λ wavelength-division-multiplexing (WDM) access network with mitigation of Rayleigh backscattering (RB) interference noise by using the orthogonal frequency division multiplexing (OFDM) modulation and intensity modulation and direct detection (IMDD) method. According to the optimal OFDM bit-loading algorithm design, the fiber chromatic dispersion and RF fading can be improved after a length fiber connection of 20 km long. Moreover, the obtained OFDM modulation rate also can meet the forward error correction (FEC) target.

Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm.

In this paper, a new method for optimal enhancement of the contrast of a medical image is proposed. The main idea is to improve the Gamma correction method to enhance and highlight the image information and the details based on a new design of the World Cup Optimization (WCO) algorithm. Gamma correction is a suitable method for contrast enhancement with an efficiency that directly depends on the correct selection of the Gamma coefficient. In this study, a newly presented algorithm was employed for optimal selection of the Gamma value by considering the entropy, edge content, and multi-objective optimization. The simulation results were compared with 5 state of the art methods for presenting method efficiency. To do this, contrast, homogeneity, weighted average peak signal-to-noise ratio (WPSNR), measure of enhancement (EME), and contrast-to-noise ratio (CNR) were employed. Final results denote that the presented multi-objective optimization algorithm improves the quality of the image contrast and can provide more details and information than the other comparable methods.

Обзор моделей параллельных вычислений

This survey aims to present the state of the art in analytic parallel computation models, providing sufficiently detailed descriptions of particularly noteworthy efforts. Such models allow predicting the computation time, speedup, efficiency and scalability of parallel algorithms for various target multiprocessor platforms. Modeling the cost of computations and communications in multiprocessor systems is an important and challenging problem. It provides insights into the design of the parallel algorithms for optimization of their deployment in the increasingly complex high-performance computing. The survey shows the evolution of parallel computing models inspired by the evolution of multiprocessor systems, from single-level models with shared memory to multi-level hierarchical models with distributed memory, which correspond to multicore clusters. The review concludes with prospective directions for further research in the area of developing mathematical models for parallel computing.

Multi-GPU Based Parallel Design of the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images.

Spectral unmixing is a vital procedure in hyperspectral remote sensing image exploitation. The linear mixture model has been widely utilized to unmix hyperspectral images by extracting a set of pure spectral signatures, called endmembers in hyperspectral jargon, and estimating their respective fractional abundances in each pixel of the scene. Many algorithms have been proposed to extract endmembers automatically, which is a critical step in the spectral unmixing chain. In recent years, the ant colony optimization (ACO) algorithm has been developed for endmember extraction from hyperspectral data, which was regarded as a combinatorial optimization problem. Although the ACO for endmember extraction (ACOEE) can acquire accurate endmember results, its high computational complexity has limited its application in the hyperspectral data analysis. The GPUs parallel computing technique can be utilized to improve the computational performance of ACOEE, but the architecture of GPUs determines that the ACOEE should be redesigned to take full advantage of computing resources on GPUs. In this paper, a multiple sub-ant-colony-based parallel design of ACOEE was proposed, in which an innovative mechanism of local pheromone for sub-ant-colonies is utilized to enable ACOEE to be preferably executed on the multi-GPU system. The proposed method can avoid much synchronization among different GPUs to affect the computational performance improvement. The experiments on two real hyperspectral datasets demonstrated that the computational performance of ACOEE significantly benefited from the proposed methods.

Towards a Combination of Low Rank and Sparsity in EIT Imaging

Electrical impedance tomography (EIT) calculates the internal conductivity distribution of a body using electrical contact measurement and has become increasingly attractive in the biomedical field. However, the design of optimal tomography image reconstruction algorithms has not achieved an adequate level of progress and maturity. The spatial-temporal properties are crucial for the improvement of reconstruction quality and efficiency in dynamic EIT reconstruction. However, these properties have not been fully utilized in previous research. In this paper, a mathematical model for EIT reconstruction is built upon a combination of the low-rank and the sparsity theories. In addition to the low-rank method based on the nuclear norm constraint, the patch-based sparse method is also used to obtain the spatial features of a reconstructed image, according to the characteristic of an irregular boundary for the EIT image. The mathematical model of the new method is solved using the variable split (VS) algorithm. The imaging results are compared with the reconstruction results of the traditional algorithms. The experimental results demonstrate better performance of the new method compared with the traditional methods. The effectiveness of the proposed scheme is verified.

Distributed Holistic Framework for Smart City Infrastructures: Tale of Interdependent Electrified Transportation Network and Power Grid

Plug-in Electric Vehicles (PEVs) play a pivotal role in transportation electrification. The flexible nature of PEVs' charging demand can be utilized for reducing charging cost as well as optimizing the operating cost of power and transportation networks. Utilizing charging flexibility of geographically spread PEVs requires design and implementation of efficient optimization algorithms. There is a synergy between electro mobility (e-Mobility) infrastructures (including charging stations) and PEVs. In this paper, we introduce a holistic framework to model interdependent nature of power systems and electrified transportation networks, enhance the operational performance of these systems as a network-of-networks, and explain the required information exchange via coupling agents (e.g., PEVs and charging stations). We develop a holistic framework that enables distributed coordination of interdependent networks through the IoT lens. To this end, we propose to use a fully distributed consensus+innovations approach. This iterative algorithm achieves a distributed solution of the decision making for each agent through local computations and limited communication with other neighboring agents that are influential in that specific decision. For instance, the optimal routing decision of a PEV involves a different set of agents as compared with the optimal charging strategy of the same PEV. The exogenous information from an external network/agent can affect internal operation of the other agents. For instance, having some information about traffic congestion at the transportation networks changes the decision of PEVs to charge their battery at another location. Our approach constitutes solving an iterative problem, which utilizes communication at the smart city layer, as a network of infrastructures, including power grid and electrified transportation network, that enables fully distributed coordination of agents. Fully distributed decision making enables scalability of the solution and plug-and-play capability, as well as increasing the information privacy of PEVs by only requiring limited local information exchange with neighboring agents. We investigate a detailed application of our framework for interdependent power systems and electrified transportation networks. To this end, we first identify the functionalities, constraints, objectives, and decision variables of each network. Then, we investigate the interdependent interactions among these networks and their corresponding agents.

Dynamic Analysis of a 7 DOF Robot Using Fuzzy Logic for Inverse Kinematics Problem

In this paper, a dynamic analysis for a redundant, serial robot, with seven degrees of freedom is presented. The dynamic model of the robot is based on importing a tri-dimensional CAD model of the robot into MATLAB®, Simulink®-Simscape™-SimMechanics™. One can import complete CAD assemblies, including all masses, inertias, joints, constraints, and tri-dimensional geometries, into the model. The first step for executing a dynamic analysis is to resolve the Inverse Kinematics (IK) problem for the redundant robot. Because this type of robot has seven degrees of freedom the IK resolution implies an infinite number of solutions. So, to be able to resolve the IK for this kind of robot a Fuzzy Logic (FL) technique is used for redundancy resolution. The entire IK resolution is based on a fact that one degree of freedom is determined using FL. Solving the inverse kinematics problem is a mandatory step in the dynamic analysis of the robot, this is required to drive the robot on certain user-imposed trajectories. The dynamic model of the robot is necessary to determine and analyse joint torques so one can reconstruct the fuzzy model with new rules. Reconstructing the fuzzy model would lead to joint torques minimization and efficiency in robot operation. Also, the dynamic model of the redundant robot is necessary for the simulation of motion, analysis of the robot’s structure and design of optimal control algorithms. This will be presented in some further research.

Optimal Shipping Path Algorithm Design for Coastal Port

As coastal port shipping industry is facing severe economic competition currently; coastal port container transportation companies are unable to increase profits by raising container’s mileage freight rate substantially as before. Against such a background, on the premise that the container transportation of coastal ports could arrive at destinations safely, the paper makes improvements on current optimal path algorithm for coastal ports, extends the meaning of optimal path such as the shortest distance, the lowest cost as well as the overall evaluation of path and cost, and made improved verification of the algorithm. By addressing the problem of choosing optimal path for coastal port shipping, the paper aims to promote the quick reaction of container shipping, reduce operation cost, enhance the competitiveness of coastal port container shipping and the economic efficiency of the algorithm, while providing new thoughts for the application of the algorithm.

Sum Rate Maximization for Multi-Carrier SWIPT Relay System With Non-Ideal Power Amplifier and Circuit Power Consumption

In this paper, we investigate the resource optimization algorithm design in a multicarrier relay system with simultaneous wireless information and power transfer (SWIPT). The relay is capable of harvesting energy from the source's signals by using the power splitting method. The non-ideal energy consumption including both the non-ideal power amplifier and non-ideal circuit power consumption is considered. First, we study the transmission rate maximization problem (TRMP) in an asymmetric decode-and-forward (DF) relay transmission, where the transmission power at the source, the transmission power at the relay, the power splitting ratio, and the transmission time are jointly optimized. The formulated problem is a non-convex problem, and it is generally quite difficult to solve it. By exploiting the structure of the problem, we propose two methods (logarithmic operation on constraints and logarithmic change of variables) to transform it into the corresponding difference of convex (DC) optimization problems. Then, we extend the TRMP to an amplify-and-forward (AF) relay transmission. Furthermore, we propose an effective algorithm to solve the DC optimization problem and prove that the algorithm can converge to a stationary point. Finally, extensive simulations are conducted to verify the performance of the proposed algorithm. The simulation results show that the asymmetric DF relay transmission achieves the highest sum rate and the AF relay transmission achieves a much lower sum rate than both the asymmetric and symmetric DF relay transmissions under different conditions.

Design of multi-drug combinations for poly-pharmacological effects using composition-activity relationship modeling and multi-objective optimization approach: Application in traditional Chinese medicine.

In recent years, multi-component therapies are increasingly utilized to treat complex diseases such as cancer, diabetes, and other chronic complex diseases. Here, we proposed the protocol for rational design of drug combination with poly-pharmacological effects by integration of design of experiments (DOE), computational modeling, and multiple-objective optimization algorithm. Here, we introduce a common workflow for modeling quantitative relationship of chemical composition and multiple activities of drug combinations. As an example, anti-oxidation, neuroprotective, and anti-platelet activities of three different salvia polyphenols were measured, which were mathematically represented by multivariant regression models to evaluate the effect of combination. In validation, the optimized combination which obtained by weighed-sum method showed good activities in all three models. Our results demonstrate that the multiple-objective optimization strategy is suitable to optimize the ratio of the compounds so to induce the best therapeutic action.