Records with Subject: Optimization
Showing records 1 to 25 of 73. [First] Page: 1 2 3 Last
At what pressure shall CO2 be transported by ship? An in-depth cost comparison of 7 and 15 barg shipping.
Simon Roussanaly, Han Deng, Geir Skaugen, Truls Gundersen
July 7, 2021 (v1)
Subject: Optimization
Keywords: Carbon Capture and Storage, CO2 shipping, CO2 transport, Optimal transport pressure, Technoeconomic Analysis
While pipeline transport traditionally has been regarded as the best option for CO2 transport due to its low cost over short distances and important economies of scale, interest in vessel-based transport of CO2 is growing. While virtually all recent literature has focused on low pressure transport (at 7 barg and -46°C), the issue of optimal transport conditions, in terms of pressure, temperature and gas composition, is becoming more relevant as carbon capture and storage chains based on ship transport move closer towards implementation.
This study focuses on an in-depth comparison of the two primary and relevant transport pressures, 7 and 15 barg, for annual volumes up to 20 MtCO2/y and transport distances up to 2000 km. We also address the impact of a number of key factors on optimal transport conditions, including (a) transport between harbours versus transport to an offshore site, (b) CO2 pressure prior to conditioning, (c) the presence of impurities and of purity constraints, and... [more]
Fitness Landscape Analysis and Edge Weighting-Based Optimization of Vehicle Routing Problems
László Kovács, Anita Agárdi, Tamás Bányai
May 17, 2021 (v1)
Subject: Optimization
Keywords: fitness landscape, Optimization, traveling salesman problem, vehicle routing problem
Vehicle routing problem (VRP) is a highly investigated discrete optimization problem. The first paper was published in 1959, and later, many vehicle routing problem variants appeared to simulate real logistical systems. Since vehicle routing problem is an NP-difficult task, the problem can be solved by approximation algorithms. Metaheuristics give a “good” result within an “acceptable” time. When developing a new metaheuristic algorithm, researchers usually use only their intuition and test results to verify the efficiency of the algorithm, comparing it to the efficiency of other algorithms. However, it may also be necessary to analyze the search operators of the algorithms for deeper investigation. The fitness landscape is a tool for that purpose, describing the possible states of the search space, the neighborhood operator, and the fitness function. The goal of fitness landscape analysis is to measure the complexity and efficiency of the applicable operators. The paper aims to invest... [more]
MILP Formulation for Solving and Initializing MINLP Problems Applied to Retrofit and Synthesis of Hydrogen Networks
Patrícia R. da Silva, Marcelo E. Aragão, Jorge O. Trierweiler, Luciane F. Trierweiler
March 1, 2021 (v1)
Subject: Optimization
Keywords: hydrogen network, initialization strategy, mathematical programming, MILP optimization, MINLP optimization, virtual compressor approach
The demand for hydrogen in refineries is growing due to its importance as a sulfur capture element. Therefore, hydrogen management is critical for fulfilling demands as efficiently as possible. Through mathematical modeling, hydrogen network management can be better performed. Cost-efficient Mixed-Integer Linear Programming (MILP) and Mixed-Integer Nonlinear Programming (MINLP) optimization models for (re)designing were proposed and implemented in GAMS with two case studies. Linear programming has the limitation of no stream mixing allowed; therefore, to overcome this limitation, an algorithm-based procedure called the Virtual Compressor Approach was proposed. Based on the MILP optimal solution obtained, the streams and compressors were merged. As a result, the number of compressors was reduced, along with the inherent investment costs. An operational cost reduction of more than 28% (example 1) and 26% (example 2) was obtained with a linear model. The optimal MILP solution after rearra... [more]
Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems
Gustavo Meirelles, Bruno Brentan, Joaquín Izquierdo, Edevar Luvizotto Jr
December 22, 2020 (v1)
Subject: Optimization
Keywords: benchmarking problems, Optimization, swarm optimization
Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The... [more]
Data-driven Spatial Branch-and-bound Algorithm for Box-constrained Simulation-based Optimization
Jianyuan Zhai, Fani Boukouvala
November 14, 2020 (v1)
Subject: Optimization
Keywords: Black-box Optimization, Branch-and-bound, Simulation-based Optimization
The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of equation-based optimization solvers may be impractical and data-driven optimization techniques are often needed. In this work, we present a novel data-driven spatial branch-and-bound algorithm for simulation-based optimization problems with box constraints, aiming for consistent globally convergent solutions. The main contribution of this paper is the introduction of the concept data-driven convex underestimators of data and surrogate functions, which are employed within a spatial branch-and-bound algorithm. The algorithm is showcased by an illustrative example and is then extensively studied via computational experiments on a large set of benchmark problems.
Development of an Optimal Path Algorithm for Construction Equipment
Hak June Lee, So Young Lim
August 29, 2020 (v1)
Subject: Optimization
Keywords: algorithm, construction, dump, Modelling, optimal path, safety, terrain
The fourth industrial revolution based on information and communication technology (ICT and IoT) is converging into the overall realm of technology, economy and society, creating innovative changes. In line with these changes, research is being actively carried out to integrate information and communication with automation at construction sites. This study was started to analyze problems arising from inefficient operation of construction equipment through analysis of risks arising at construction sites and to provide solutions related to these problems. In order to provide the optimal route of movement of construction equipment, an expert survey was conducted and an algorithm was developed to establish the optimal route of movement by analyzing the weights for each item of the survey. The adequacy of the algorithm was determined by comparing the developed algorithm with the actual data of the construction site in operation, and a safe and productive route as well as problems related to... [more]
Advances in Theoretical and Computational Energy Optimization Processes
Ferdinando Salata, Iacopo Golasi
August 29, 2020 (v1)
Subject: Optimization
Industry, construction and transport are the three sectors that traditionally lead to the highest energy requirements [...]
An Improved Artificial Electric Field Algorithm for Multi-Objective Optimization
Hemant Petwal, Rinkle Rani
July 17, 2020 (v1)
Subject: Optimization
Keywords: artificial electric field algorithm, fine-grained elitism selection, multi-objective optimization problems, recombination operator, shift-based density estimation, strength Pareto
Real-world problems such as scientific, engineering, mechanical, etc., are multi-objective optimization problems. In order to achieve an optimum solution to such problems, multi-objective optimization algorithms are used. A solution to a multi-objective problem is to explore a set of candidate solutions, each of which satisfies the required objective without any other solution dominating it. In this paper, a population-based metaheuristic algorithm called an artificial electric field algorithm (AEFA) is proposed to deal with multi-objective optimization problems. The proposed algorithm utilizes the concepts of strength Pareto for fitness assignment and the fine-grained elitism selection mechanism to maintain population diversity. Furthermore, the proposed algorithm utilizes the shift-based density estimation approach integrated with strength Pareto for density estimation, and it implements bounded exponential crossover (BEX) and polynomial mutation operator (PMO) to avoid solutions tra... [more]
New Model-Based Analysis Method with Multiple Constraints for Integrated Modular Avionics Dynamic Reconfiguration Process
Zeyong Jiang, Tingdi Zhao, Shihai Wang, Hongyan Ju
July 17, 2020 (v1)
Subject: Optimization
Keywords: AADL, analysis method, dynamic reconfiguration, multi-constraint, Petri net
With the development of integrated modular avionics (IMA), the dynamic reconfiguration of IMA not only provides great advantages in resource utilization and aircraft configuration, but also acts as a valid means for resource failure management. It is vital to ensure the correction of the IMA dynamic reconfiguration process. The analysis of the dynamic reconfiguration process is a significant task. The Architecture Analysis & Design Language (AADL) is widely used in complicated real-time embedded systems. The language can describe the system configuration and the execution behaviors, such as configuration changes. Petri net is a widely used tool to conduct simulation analysis in many aspects. In this study, a model-based analyzing method with multiple constraints for the IMA dynamic reconfiguration process was proposed. First, several design constraints on the process were investigated. Second, the dynamic reconfiguration process was modeled based on the AADL. Then, a set of rules for t... [more]
Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review
Gade Pandu Rangaiah, Zemin Feng, Andrew F. Hoadley
July 2, 2020 (v1)
Subject: Optimization
Keywords: chemical engineering, multi-objective optimization, multiple criteria, non-dominated solutions, optimization procedure, optimization software, optimization techniques, Pareto optimal front, Pareto ranking, process engineering
This tutorial and review of multi-objective optimization (MOO) gives a detailed explanation of the 5 steps to create, solve, and then select the optimum result. Unlike single-objective optimization, the fifth step of selection or ranking of solutions is often overlooked by the authors of papers dealing with MOO applications. It is necessary to undertake a multi-criteria analysis to choose the best solution. A review of the recent publications using MOO for chemical process engineering problems shows a doubling of publications between 2016 and 2019. MOO applications in the energy area have seen a steady increase of over 20% annually over the last 10 years. The three key methods for solving MOO problems are presented in detail, and an emerging area of surrogate-assisted MOO is also described. The objectives used in MOO trade off conflicting requirements of a chemical engineering problem; these include fundamental criteria such as reaction yield or selectivity; economics; energy requireme... [more]
Quadratic Interpolation Based Simultaneous Heat Transfer Search Algorithm and Its Application to Chemical Dynamic System Optimization
Ebrahim Alnahari, Hongbo Shi, Khalil Alkebsi
June 23, 2020 (v1)
Subject: Optimization
Keywords: chemical engineering processes, dynamic system optimization, global optimization, heat transfer search algorithm, quadratic interpolation
Dynamic optimization problems (DOPs) are widely encountered in complex chemical engineering processes. However, due to the existence of highly constrained, nonlinear, and nonsmooth environment in chemical processes, which usually causes nonconvexity, multimodality and discontinuity, handling DOPs is not a straightforward task. Heat transfer search (HTS) algorithm is a relative novel metaheuristic approach inspired by the natural law of thermodynamics and heat transfer. In order to solve DOPs efficiently, a new variant of HTS algorithm named quadratic interpolation based simultaneous heat transfer search (QISHTS) algorithm is proposed in this paper. The QISHTS algorithm introduces three modifications into the original HTS algorithm, namely the effect of simultaneous heat transfer search, quadratic interpolation method, and population regeneration mechanism. These three modifications are employed to provide lower computational complexity, as well as to enhance the exploration and exploit... [more]
A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems
Ai-Qing Tian, Shu-Chuan Chu, Jeng-Shyang Pan, Yongquan Liang
May 22, 2020 (v1)
Subject: Optimization
Keywords: meta-heuristic algorithm, MPPT, Particle Swarm Algorithm, Pigeon-Inspired Optimization
The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power−voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a new type of algorithm that combines a new pigeon population algorithm named Parallel and Compact Pigeon-Inspired Optimization (PCPIO) with MPPT, which can solve the problem that MPPT cannot reach the near global maximum power point. This hybrid algorithm is fast, stable, and capable of globally optimizing the maximum power point tracking algorithm. Therefore, the purpose of this article is to study the performance of two optimization techniques. The two algorithms are Particle Swarm Algorithm (PSO) and improved pigeon algorithm. This paper first studies the mechanism of multi-peak output characteristics of photovoltaic arrays in complex environments, and then proposes a multi-... [more]
Optimization Methods for the Extraction of Vegetable Oils: A Review
Divine Bup Nde, Anuanwen Claris Foncha
April 14, 2020 (v1)
Subject: Optimization
Keywords: experimental designs and optimization, oil extraction, oilseeds, optimization software, polynomial modelling
Most seed oils are edible while some are used generally as raw material for soap production, chocolate, margarine, and recently in biodiesel formulations as potential candidates capable of replacing fossil fuels which are costly and destructive to the environment. Oilseeds are a green and major reservoir which when properly exploited can be used sustainably for the production of chemicals at both the laboratory and industrial scales. Oil extraction is one of the most critical steps in seed oil processing because it determines the quality and quantity of oil extracted. Optimization of the extraction conditions for each extraction method enhances yield and quality meanwhile a carefully chosen optimization process equally has the potential of saving time and heat requirements with an associated consequence on cost reduction of the entire process. In this review, the techniques used to optimize oil extraction from plant materials which can be consulted by stakeholders in the field are brou... [more]
Recent Advances on Optimization for Control, Observation, and Safety
Guillermo Valencia-Palomo, Francisco-Ronay López-Estrada, Damiano Rotondo
April 14, 2020 (v1)
Subject: Optimization
Mathematical optimization is the selection of the best element in a set with respect to a given criterion [...]
Modern Modeling Paradigms Using Generalized Disjunctive Programming
Qi Chen, Ignacio Grossmann
December 16, 2019 (v1)
Subject: Optimization
Keywords: generalized disjunctive programming, mathematical programming, MINLP, process design, process modeling
Models involving decision variables in both discrete and continuous domain spaces are prevalent in process design. Generalized Disjunctive Programming (GDP) has emerged as a modeling framework to explicitly represent the relationship between algebraic descriptions and the logical structure of a design problem. However, fewer formulation examples exist for GDP compared to the traditional Mixed-Integer Nonlinear Programming (MINLP) modeling approach. In this paper, we propose the use of GDP as a modeling tool to organize model variants that arise due to characterization of different sections of an end-to-end process at different detail levels. We present an illustrative case study to demonstrate GDP usage for the generation of model variants catered to process synthesis integrated with purchasing and sales decisions in a techno-economic analysis. We also show how this GDP model can be used as part of a hierarchical decomposition scheme. These examples demonstrate how GDP can serve as a u... [more]
Symmetry Detection for Quadratic Optimization Using Binary Layered Graphs
Georgia Kouyialis, Xiaoyu Wang, Ruth Misener
December 16, 2019 (v1)
Subject: Optimization
Keywords: quadratic optimization, quadratically-constrained quadratic optimization, symmetry
Symmetry in mathematical optimization may create multiple, equivalent solutions. In nonconvex optimization, symmetry can negatively affect algorithm performance, e.g., of branch-and-bound when symmetry induces many equivalent branches. This paper develops detection methods for symmetry groups in quadratically-constrained quadratic optimization problems. Representing the optimization problem with adjacency matrices, we use graph theory to transform the adjacency matrices into binary layered graphs. We enter the binary layered graphs into the software package nauty that generates important symmetric properties of the original problem. Symmetry pattern knowledge motivates a discretization pattern that we use to reduce computation time for an approximation of the point packing problem. This paper highlights the importance of detecting and classifying symmetry and shows that knowledge of this symmetry enables quick approximation of a highly symmetric optimization problem.
Simulation versus Optimisation: Theoretical Positions in Energy System Modelling
Henrik Lund, Finn Arler, Poul Alberg Østergaard, Frede Hvelplund, David Connolly, Brian Vad Mathiesen, Peter Karnøe
December 10, 2019 (v1)
Subject: Optimization
Keywords: energy system analysis, investment optimisation models, modelling theory, Renewable and Sustainable Energy, simulations models
In recent years, several tools and models have been developed and used for the design and analysis of future national energy systems. Many of these models focus on the integration of various renewable energy resources and the transformation of existing fossil-based energy systems into future sustainable energy systems. The models are diverse and often end up with different results and recommendations. This paper analyses this diversity of models and their implicit or explicit theoretical backgrounds. In particular, two archetypes are defined and compared. On the one hand, the prescriptive investment optimisation or optimal solutions approach. On the other hand the analytical simulation or alternatives assessment approach. Awareness of the dissimilar theoretical assumption behind the models clarifies differences between the models, explains dissimilarities in results, and provides a theoretical and methodological foundation for understanding and interpreting results from the two archety... [more]
Economic Optimization of Component Sizing for Residential Battery Storage Systems
Holger C. Hesse, Rodrigo Martins, Petr Musilek, Maik Naumann, Cong Nam Truong, Andreas Jossen
December 10, 2019 (v1)
Subject: Optimization
Keywords: battery aging, battery energy storage system, cost analysis, economic analysis, linear programming, Lithium-Ion battery, photovoltaic panel, residential battery, size optimization
Battery energy storage systems (BESS) coupled with rooftop-mounted residential photovoltaic (PV) generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available. The manifold BESS deployed to date rely on a variety of different battery technologies, show a great variation of battery size, and power electronics dimensioning. However, given today’s high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a PV-BESS depends also on the battery operation, storage technology, and aging of the system. In this paper, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Using a linear optimization method, a cost-optimal sizing of the battery and power electronics is derived based on sola... [more]
Optimization of External Envelope Insulation Thickness: A Parametric Study
Eleftheria Touloupaki, Theodoros Theodosiou
December 10, 2019 (v1)
Subject: Optimization
Keywords: cost-optimal, Energy Efficiency, energy performance of buildings (EPBD) recast, energy policy, insulation thickness, nearly zero energy buildings (nZEBs)
Almost four years after the implementation deadline of the energy performance of buildings Directive recast (2010/31/EU) and after being referred to the Court of Justice of the EU by the European Commission, Greece has not yet proceeded with the necessary calculations and legislative measures on the minimum, cost-optimal energy performance requirements for buildings. This paper aims to identify the optimal thickness of insulation that is cost-effective to apply in urban multi-family domestic buildings in the four climate zones of Greece. A reference building is selected in order to perform calculations over ten scenarios of external insulation thickness for each climate zone on a basic and three sensitivity analysis calculations according to the EU comparative methodology framework. The resulting energy savings for each insulation scenario are calculated, and then the cost-effectiveness of the measure is examined in financial and macroeconomic perspective for an economic lifecycle of 3... [more]
Optimization of Microwave Coupled Hot Air Drying for Chinese Yam Using Response Surface Methodology
Hanyang Wang, Dan Liu, Haiming Yu, Donghai Wang, Jun Li
December 10, 2019 (v1)
Subject: Optimization
Keywords: Chinese yam, microwave coupled hot air, process optimization, rehydration ratio, total sugar content
The effect of microwave coupled hot air drying on rehydration ratio (RR) and total sugar content (TSC) of Chinese yam was investigated. Single factor test and response surface methodology were used for process parameter optimization with hot air temperature, hot air velocity, slice thickness, and microwave power density as variables and RR and TSC of dried products as responses. The effect of variables on RR followed the order: slice thickness > hot air temperature > microwave power density > hot air velocity. The effect of variables on TSC followed the order: slice thickness > microwave power density > hot air velocity > hot air temperature. The optimized process parameters were hot air velocity of 2.5 m/s, hot air temperature of 61.7 °C, slice thickness of 8.5 mm, and microwave power density of 5.9 W/g. Under the optimal conditions, the predicted values of RR and TSC were 1.90 g/g and 5.74 g/100 g, respectively, which is very close to corresponding actual values (1.83 g/g and 5.72 g/... [more]
Fine-Tuning Meta-Heuristic Algorithm for Global Optimization
Ziyad T. Allawi, Ibraheem Kasim Ibraheem, Amjad J. Humaidi
December 3, 2019 (v1)
Subject: Optimization
Keywords: benchmark functions, exploitation, exploration, global minimum, global optimization, local minimum, meta-heuristics, swarm intelligence
This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that if one step improves the solution, then it is unnecessary to execute the remaining steps. The performance of the proposed FTMA has been compared with that of five other optimization algorithms over ten benchmark test functions. Nine of them are well-known and already exist in the literature, while the tenth one is proposed by the authors and introduced in this article. One test trial was shown to check the performance of each algorithm, and the other test for 30 trials to measure the statistical results of the performance of the proposed algorithm against the others. Results confirm that the proposed FTMA global optimization algorithm has a competi... [more]
Pelletization of Sunflower Seed Husks: Evaluating and Optimizing Energy Consumption and Physical Properties by Response Surface Methodology (RSM)
Xuyang Cui, Junhong Yang, Xinyu Shi, Wanning Lei, Tao Huang, Chao Bai
November 24, 2019 (v1)
Subject: Optimization
Keywords: energy consumption, pelletization, performance evaluation, RSM optimization, sunflower seed husk
Pelletization is a significant approach for the efficient utilization of biomass energy. Sunflower seed husk is a common solid waste in the process of oil production. The novelty of this study was to determine the parameters during production of a novel pellet made from sunflower seed husk. The energy consumption (W) and physical properties (bulk density (BD) and mechanical durability (DU)) of the novel pellet were evaluated and optimized at the laboratory by using a pelletizer and response surface methodology (RSM) under a controlled moisture content (4%−14%), compression pressure (100−200 MPa), and die temperature (70−170 °C). The results show that the variables of temperature, pressure, and moisture content of raw material are positively correlated with BD and DU. Increasing the temperature and moisture content of raw materials can effectively reduce W, while increasing the pressure has an adverse effect on W. The optimum conditions of temperature (150 °C), pressure (180 MPa), and m... [more]
Towards Quality by Design to recover high-quality products from waste and wastewater streams
Céline Vaneeckhaute
November 2, 2019 (v1)
Subject: Optimization
Keywords: Mathematical modelling, Optimization, Process control, Product quality, Quality by Design, Resource Recovery
Recovering nutrients from wastewaters and wastes, such as sewage sludge and food waste, as sustainable bio-based products provides a key solution to major environmental problems. Classical technology development approaches for resource recovery largely ignore the real-world variability in raw waste materials, which currently hinders the successful implementation of recovery strategies. A major challenge is to create a consistent, sustainable and environmentally friendly supply of high-quality end-products that can compete with fossil-derived products currently on the market. There is urgent need for a paradigm shift from classical technology development approaches to sustainable integrated end-user focused strategies, supported by a reliable, competitive and repeatable quality assurance framework. An improved balance between efficiency and cost in bio-based production chains is needed, while continuously assuring product quality and safety. This
presentation suggests the use of a qual... [more]
Finite Time Thermodynamic Optimization of an Irreversible Proton Exchange Membrane Fuel Cell for Vehicle Use
Changjie Li, Ye Liu, Bing Xu, Zheshu Ma
September 13, 2019 (v1)
Subject: Optimization
Keywords: finite time thermodynamic model, irreversibility, operating parameters, optimal performance, PEMFC
A finite time thermodynamic model of an irreversible proton exchange membrane fuel cell (PEMFC) for vehicle use was established considering the effects of polarization losses and leakage current. Effects of operating parameters, including operating temperature, operating pressure, proton exchange membrane water content, and proton exchange membrane thickness, on the optimal performance of the irreversible PEMFC are numerically studied in detail. When the operating temperature of the PEMFC increases, the optimal performances of PEMFC including output power density, output efficiency, ecological objective function, and ecological coefficient of performance, will be improved. Among them, the optimal ecological objective function increased by 81%. The proton film thickness has little effect on the output efficiency and the ecological of coefficient performance. The maximum output power density increased by 58% as the water content of the proton exchange membrane increased from 50% to the s... [more]
Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization
Rashida Adeeb Khanum, Muhammad Asif Jan, Nasser Tairan, Wali Khan Mashwani, Muhammad Sulaiman, Hidayat Ullah Khan, Habib Shah
August 8, 2019 (v1)
Subject: Optimization
Keywords: adaptive differential evolution, evolutionary computation, external archives, global search, hybridization, local search, metaheuristics, Optimization, population minimization
Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon−Fletcher−Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the ar... [more]
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