Competitive Objective Functions Research Articles

A Constrained Louvain Algorithm with a Novel Modularity

Community detection is a significant and challenging task in network research. Nowadays, many community detection methods have been developed. Among them, the classical Louvain algorithm is an excellent method aiming at optimizing an objective function. In this paper, we propose a modularity function F2 as a new objective function. Our modularity function F2 overcomes certain disadvantages of the modularity functions raised in previous literature, such as the resolution limit problem. It is desired as a competitive objective function. Then, the constrained Louvain algorithm is proposed by adding some constraints to the classical Louvain algorithm. Finally, through the comparison, we have found that the constrained Louvain algorithm with F2 is better than the constrained Louvain algorithm with other objective functions on most considered networks. Moreover, the constrained Louvain algorithm with F2 is superior to the classical Louvain algorithm and the Newman’s fast method.

Bayesian Optimization for Field-Scale Geological Carbon Storage

We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization (BO) for injection well scheduling optimization in geological carbon sequestration. This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage. The implicit parallel accurate reservoir simulator (IPARS) is utilized to accurately capture the underlying physical processes during CO 2 sequestration. IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations. In this work, we use the compositional flow module to simulate the geological carbon storage process. The compositional flow model, which includes a hysteretic three-phase relative permeability model, accounts for three major CO 2 trapping mechanisms: structural trapping, residual gas trapping, and solubility trapping. Furthermore, IPARS is coupled to the International Business Machines (IBM) Corporation Bayesian Optimization Accelerator (BOA) for parallel optimizations of CO 2 injection strategies during field-scale CO 2 sequestration. BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm—the Gaussian process regression, and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample. The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel (batch) executions, scales better for high-dimensional problems, and is more robust to initializations. We demonstrate these merits by applying the algorithm in the optimization of the CO 2 injection schedule in the Cranfield site in Mississippi, USA, using field data. The optimized injection schedule achieves 16% more gas storage volume and 56% less water/surfactant usage compared with the baseline. The performance of BO is compared with that of a genetic algorithm (GA) and a covariance matrix adaptation (CMA)-evolution strategy (ES). The results demonstrate the superior performance of BO, in that it achieves a competitive objective function value with over 60% fewer forward model evaluations.

An elastic constraint method for a multi-objective production-distribution planning problem with competition

Competition is one of the most important topics in supply chain management and it is considered widely in the last decades. This study aims to evaluate effects of Stackelberg and Cournot competition on integrated production-distribution (P-D) planning problem. Minimising cost and maximising satisfaction of service level are considered as two main objective functions then two competitive objective functions are developed within considered competitions. The presented competitive P-D model is totally unimodular and this feature makes the model to generate integer solutions without circumstance of integrality. Next, elastic constraint method is adopted to solve the developed competitive multi-objective model. Finally, numerical examples are conducted to evaluate capability of the proposed approach. According to the structure and assumptions of the proposed model, competition can increase cost of supply chain.

The Comparison of Lately Proposed Harris Hawks Optimization and Jaya Optimization in Solving Directional Overcurrent Relays Coordination Problem

In this paper, a lately proposed Harris Hawks Optimizer (HHO) is used to solve the directional overcurrent relays (DOCRs) coordination problem. To the best of the authors’ knowledge, this is the first time HHO is being used in the DOCRs coordination problem. The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks from different directions, based on the dynamic nature of scenarios and escaping patterns of the prey. To test its performances in solving the DOCRs coordination problem, it is adopted in 3-bus, 4-bus, 8-bus, and 9-bus systems, which are formulated by three kinds of optimization models as linear programming (LP), nonlinear programming (NLP), and mixed integer nonlinear programming (MINLP), according to the nature of the design variables. Meanwhile, another lately proposed optimization algorithm named Jaya is also adopted to solve the same problem, and the results are compared with HHO in aspects of objective function value, convergence rate, robustness, and computation efficiency. The comparisons show that the robustness and consistency of HHO is relatively better than Jaya, while Jaya provides faster convergence rate with less CPU time and occasionally more competitive objective function value than HHO.

Towards nearly Zero Energy Buildings: Shape optimization of typical housing typologies in Ibero-American temperate climate cities from a holistic perspective

This research analyses the reduction of primary energy consumption (PE)11PE: primary energy. of slab and high-rise housing typologies through the optimization of their building shapes. The process considered applies passive strategies at the concept stage of the architectural design.A quasi-steady state method provides an estimation of building PE consumption in heating and cooling. Then, a multi-objective Genetic Algorithm (GA)22GA: Genetic algorithm. optimizes the building shapes with three competitive objective functions: minimization of PE consumption per m2 (PEI), and maximization of passive volume ratio (PVR)33PVR: Passive volume ratio is the ratio between the building passive and the useful volumes., and the sum of roofs and best oriented surfaces (RBOS)44RBOS: Best oriented surfaces are those looking South for the Northern Hemisphere and North for the Southern Hemisphere, and horizontal roofs.. The performance-driven design workflow shows the Pareto front solutions that reflect the influence of passive strategies on the building shapes. A performance simulation of the single tower with Energy Plus permits to evaluate the quasi-state method sensibility. Finally, a multivariate linear regression evaluates the interdependence among the optimized parameters.The methodology is tested in four cities with temperate climate: Resistencia and Buenos Aires in Argentina, and Seville and Madrid in Spain. It is suitable for the early stages of design since it allows designers to relate architectural morphologies with environmental issues to reduce energy consumption and Green-House Gasseś emissions within the current Climate Change scenario.

An Elastic Constraint Method for a Multi-objective Production-Distribution Planning Problem with Competition

Competition is one of the most important topics in supply chain management and it is considered widely in the last decades. This study aims to evaluate effects of Stackelberg and Cournot competition on integrated production-distribution (P-D) planning problem. Minimising cost and maximising satisfaction of service level are considered as two main objective functions then two competitive objective functions are developed within considered competitions. The presented competitive P-D model is totally unimodular and this feature makes the model to generate integer solutions without circumstance of integrality. Next, elastic constraint method is adopted to solve the developed competitive multi-objective model. Finally, numerical examples are conducted to evaluate capability of the proposed approach. According to the structure and assumptions of the proposed model, competition can increase cost of supply chain.

A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for Cooperative Air Combat DWTA

A hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm is proposed to solve cooperative air combat dynamic weapon target assignment (DWTA). First, based on the threshold of damage probability and time window constraints, a new cooperative air combat DWTA multiobjective optimization model is presented, which employs the maximum of the target damage efficiency and minimum of ammunition consumption as two competitive objective functions. Second, in order to tackle the DWTA problem, a mixed MODPSO and neighborhood search algorithm is proposed. Furthermore, the repairing operator is introduced into the mixed algorithm, which not only can repair infeasible solutions but also can improve the quality of feasible solutions. Besides, the Cauchy mutation is adopted to keep the diversity of the Pareto optimal solutions. Finally, a typical two-stage DWTA scenario is performed by HMODPSO and compared with three other state-of-the-art algorithms. Simulation results verify the effectiveness of the new model and the superiority of the proposed algorithm.

Non-dominated sorting differential evolution algorithm for the minimization of route based fuel consumption multiobjective vehicle routing problems

In this paper, three parallel multi-start non-dominated sorting differential evolution algorithms (PMS-NSDEs) are proposed for the solution of four multiobjective route based fuel consumption vehicle routing problems (MRFCVRPs) and their results are compared with the results of a parallel multi-start NSGA II algorithm. All these algorithms use more than one initial population of solutions. In each algorithm a variable neighborhood search algorithm for the improvement of each solution separately is used. The problems that are formulated with two competitive objective functions are the multiobjective symmetric and asymmetric delivery route based fuel consumption vehicle routing problem (MSDRFCVRP and MADRFCVRP) and the multiobjective symmetric and asymmetric pick-up route based fuel consumption vehicle routing problem (MSPRFCVRP and MAPRFCVRP). The objective functions correspond to the optimization of the time needed for the vehicle to travel between two customers or between the customer and the depot and to the route based fuel consumption of the vehicle considering the traveled distance, the load of the vehicle, the slope of the road, the speed and the direction of the wind, and the driver’s behavior when the decision maker plans delivery or pick-up routes. A number of modified Vehicle Routing Problem instances are used in order to measure the quality of the proposed algorithms.

Multi-objective optimization of polyester-rope and steel-rope suspended footbridges

Historically, suspended footbridges have been built from ropes (i.e., cables) constructed of a variety of materials including iron and natural fibers. However, contemporary suspended footbridges are typically constructed with steel rope. One exception, a 64m span polyester-rope footbridge completed in 2013, demonstrates the potential for alternative rope materials in contemporary footbridge design and construction. The first goal of this paper is to support the idea that polyester rope has promise in future footbridge applications by comparing minimum rope volume and self-weight results for polyester-rope and steel-rope footbridges with spans ranging from 15 to 64m in two multi-objective optimization problems. In both problems the competitive objective functions are span which is maximized and rope volume which is minimized. The results are minimum volume systems for spans in the defined range. Minimizing volume reduces rope cost and eases material transport and handling. To provide an alternative measure of rope quantity, volume results are scaled to find the equivalent self-weights. This study focuses on in-plane structural behavior and investigates two-dimensional rope systems with or without prestress and with or without under-deck stays. A combination of static and natural frequency constraints is considered in the optimization problems. The second goal of this paper is to describe the novel methodology developed to evaluate these optimization problems. This methodology combines a non-dominated sorting genetic algorithm for searching the design space with dynamic relaxation and eigenanalysis algorithms for the structural analysis. Results indicate that polyester-rope systems have higher volumes, but lower self-weights than steel-rope systems. This observation supports the premise that polyester-rope footbridges are potential alternatives to steel-rope footbridges. The presented methodology can be adapted to evaluate how other unconventional materials compare to more conventional counterparts that are well established in bridge applications.

Multi-objective Optimization of the Extrusion Process

In the paper a multi-objective optimization of a porthole extrusion die used to manufactured a thick AA7003 round tube is presented. Eight competitive objective functions were selected aimed at increasing the seam weld quality, the die lifetime, the production rate and at restraining the die yielding and the peak process load with respect to an initial already optimized solution. Six geometric input variables were included in the optimization procedure (welding chamber and bridges height, ports width, undercut on ports, die entry angle, mandrel-bridges fillet radius) together with the ram speed. The multi-objective optimization was performed by means of modeFRONTIER® using meta-models generated over a selected set of experimental and numerical training designs.

Density filters for topology optimization based on the Pythagorean means

In topology optimization, restriction methods are needed to prohibit mesh dependent solutions and enforce length scale on the optimized structure. This paper presents new restriction methods in the form of density filters. The proposed filters are based on the geometric and harmonic means, respectively, and possess properties that could be of interest in topology optimization, for example the possibility to obtain solutions which are almost completely black and white. The article presents the new filters in detail, and several numerical test examples are used to investigate the properties of the new filters compared to filters existing in the literature. The results show that the new filters in several cases provide solutions with competitive objective function values using few iterations, but also, and perhaps more importantly, in many cases, different filters make the optimization converge to different solutions with close to equal value. A variety of filters to choose from will hence provide the user with several suggested optimized structures, and the new filters proposed in this work may certainly provide interesting alternatives.

게임 이론과 공진화 알고리즘에 기반한 다목적 함수의 최적화

다목적 함수 최적화 문제(Multi-objective Optimization Problems : MOPs)는 공학적인 문제를 풀고자 할 때 자주 접하게 되는 대표적인 문제 중 하나이다. 공학자들이 다루는 실세계 최적화 문제들은 몇 개의 경합하는 목적 함수(objective function) 들로 이루어진 문제일 경우가 많다. 본 논문에서는 다목적 함수 최적화 문제의 정의를 소개하고 이 문제를 풀기 위한 몇 가지 접근법을 소개한다. 먼저 서론에서는 파레토 최적해(Pareto optimal solution) 의 개념을 이용한 기존의 최적화 알고리즘과 이와는 달리 게임 이론(Game Theory) 으로부터 도출된 최적화 알고리즘인 내쉬 유전자 알고리즘(Nash Genetic Algorithm Nash GA) 그리고 본 논문에서 제안하는 공진화 알고리즘의 기반이 되는 진화적 안정 전략 (Evolutionary Stable Strategy : ESS) 의 이론적 배경을 소개한다. 또 본론에서는 다목적 함수 최적화 문제와 파레토 최적 해의 정의를 소개하고 다목적 함수 최적화 문제를 풀기 위하여 유전자 알고리즘을 진화적 게임 이론(Evolutionary Game Theory : EGT) 에 적용시킨 내쉬 유전자 알고리즘과 본 논문에서 새로이 제안하는 공진화 알고리즘의 구조를 설명하고 이 두 가지 알고리즘을 대표적인 다목적 함수 최적화 문제에 적용하고 결과를 비교 검토함으로써 진화적 게임 이론의 두 가지 아이디어 내쉬의 균형(Equilibrium) 과 진화적 안정전략 에 기반한 최적화 알고리즘들이 다목적 함수 문제의 최적해 를 탐색할 수 있음을 확인한다. Multi-objective Optimization Problems(MOPs) are occur more frequently than generally thought when we try to solve engineering problems. In the real world, the majority cases of optimization problems are the problems composed of several competitive objective functions. In this paper, we introduce the definition of MOPs and several approaches to solve these problems. In the introduction, established optimization algorithms based on the concept of Pareto optimal solution are introduced. And contrary these algorithms, we introduce theoretical backgrounds of Nash Genetic Algorithm(Nash GA) and Evolutionary Stable Strategy(ESS), which is the basis of Co-evolutionary algorithm proposed in this paper. In the next chapter, we introduce the definitions of MOPs and Pareto optimal solution. And the architecture of Nash GA and Co-evolutionary algorithm for solving MOPs are following. Finally from the experimental results we confirm that two algorithms based on Evolutionary Game Theory(EGT) which are Nash GA and Co-evolutionary algorithm can search optimal solutions of MOPs.