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Optimization of chemical synthesis with heuristic algorithms

PHYSICAL CHEMISTRY CHEMICAL PHYSICS 2023年第5期25卷 4323-4331页

作者： Chen, Jialu Xu, Wenjun Zhang, Ruiqin City Univ Hong Kong Dept Phys Hong Kong Peoples R China Beijing Computat Sci Res Ctr Beijing 100193 Peoples R China

Optimizing reaction conditions to improve the yield is fundamental for chemical synthesis and industrial processes. Experiments can only be performed under a small portion of reaction conditions for a system, so a strategy of experimental design is required. Bayesian optimization, a global optimization algorithm, was found to outperform human decision-making in reaction optimization. Similarly, heuristic algorithms also have the potential to solve optimization problems. In this work, we optimize these reaction conditions for Buchwald-Hartwig and Suzuki systems by predicting reaction yields with three heuristic algorithms and three encoding methods. Our results demonstrate that particle swarm optimization with numerical encoding is better than the genetic algorithm or simulated annealing. Moreover, its performance is comparable to Bayesian optimization without the computational costs of descriptors. Particle swarm optimization is simple and easy to perform, and it can be implemented into laboratory practice to promote chemical synthesis.

关键词： heuristic algorithms

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Path Planning Optimization of the Load Transport Process Using heuristic algorithms

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APPLIED SCIENCES-BASEL 2024年第21期14卷

作者： Kwiaton, Pawel Cekus, Dawid Skrobek, Dorian Sofer, Michal Poruba, Zdenek Czestochowa Tech Univ Fac Mech Engn Dept Mech & Machine Design Fundamentals Dabrowskiego 73 PL-42201 Czestochowa Poland Jan Dlugosz Univ Czestochowa Fac Sci & Technol Dept Adv Computat Methods Armii Krajowej 13-15 PL-42200 Czestochowa Poland VSB Tech Univ Ostrava Fac Mech Engn Dept Appl Mech 17 Listopadu 15-2127 Ostrava 70833 Czech Republic

The paper presents the process of optimizing the duty cycle of a rotary crane. The minimization of the carried load's trajectory was chosen as the objective function. The research was conducted using the genetic algorithm and the particle swarm algorithm. The influence of particular algorithm parameters on the obtained optimal solution was characterized. For the obtained best case, the inverse kinematics problem was solved, allowing us to determine the control functions of individual crane members. The presented redundant system was solved with the use of an algorithm for temporarily limiting the movement of specific kinematic pairs. On the basis of the obtained results, it was determined which of the algorithms used is more favorable, taking into account the crane's operational safety and lifting capacity.

关键词： heuristic algorithms optimization load transport PSO genetic algorithm

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Applications of Novel heuristic algorithms in Design Optimization of Energy-Efficient Distribution Transformer

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IEEE ACCESS 2023年 11卷 15968-15980页

作者： Hashemi, Mohammad Hassan Kilic, Ulas Dikmen, Selim Ege Univ Elect & Elect Engn Dept TR- 35040 Izmir Turkiye Eltas Transformator AS Res & Dev Dept TR-35800 Izmir Turkiye

Transformers are crucial and expensive assets of power grids. Reducing power losses in power and distribution transformers is important because it increases the efficiency of the transformer, which in turn reduces the costs for the utility company and consumers. Losses in the transformer generate heat, which can reduce the lifespan of the transformer and require additional cooling. Additionally, reducing losses can help to decrease greenhouse gas emissions associated with the generation of electricity. This study presents an optimization method for transformer design problem using variables that have a great impact on the performance of a transformer. Due to the non-convex nature of the transformer design problems, the empirical methods fail to find the optimal solution and the design process is very tedious and time-consuming. Considering No Free Lunch (NFL) theorem, the design problem is solved using four novel heuristic optimization algorithms, the Firefly Optimization Algorithm (FA), Arithmetic Optimization Algorithm (AOA), Grey Wolf Optimization Algorithm (GWO), and Artificial Gorilla Troops Optimizer Algorithm (GTO) and the results are compared to an already manufactured 1000 kVA eco-friendly distribution transformer using the empirical methods. The outcome of the optimization shows that the suggested method along with the algorithms mentioned leads to a notable decrease in power losses by up to 3.5%, and a reduction in transformer weight by up to 8.3%. This leads to an increase in efficiency, decreased costs for materials, longer lifespan and a reduction in emissions. The developed model is capable of optimally designing oil-immersed distribution transformers with different power ratings and voltage levels.

关键词： Oil insulation Optimization Costs Power transformer insulation Transformer cores heuristic algorithms Finite element analysis Ecodesign Transformers Eco-design transformer energy efficiency optimization power losses transformer design

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NEW heuristic algorithms FOR EFFICIENT HIERARCHICAL PATH PLANNING

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IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION 1991年第1期7卷 9-26页

作者： ZHU, D LATOMBE, JC Robotics Laboratory Computer Science Department University of Stanford Stanford CA USA

One of the ultimate goals of robotics research is to create autonomous robots. Progress toward this goal requires advances in many domains, including automatic motion planning. The "basic problem" in motion planning is to construct a collision-free path for a moving object among fixed obstacles. In this paper, we consider one of the most popular approaches to path planning: hierarchical approximate cell decomposition. This approach consists of constructing successive decompositions of the robot's configuration space into rectangloid cells and searching the connectivity graph built at each level of decomposition for a path. Despite its conceptual simplicity, an efficient implementation of this approach raises many delicate questions that have not yet been addressed. The major contributions of this paper are 1) a novel approach to cell decomposition based on "constraint reformulation" and 2) a new algorithm for hierarchical search with a mechanism for recording failure conditions. We have implemented these algorithms in a path planner and experimented with this planner on various examples (some of which are reported in this paper). These experiments show that our planner is significantly (approximately 10 times) faster than previous planners based on the same general approach.

关键词： heuristic algorithms Path planning Robotics and automation Motion planning Orbital robotics Robots Humans Automatic control Computational Intelligence Society Computer science

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EXACT AND heuristic algorithms FOR THE MINIMIZATION OF INCOMPLETELY SPECIFIED STATE MACHINES

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 1994年第2期13卷 167-177页

作者： RHO, JK HACHTEL, GD SOMENZI, F JACOBY, RM CADENCE DESIGN SYST LOWELL MA 01852 USA

In this paper me present two exact algorithms for state minimization of FSM's. Our results prove that exact state minimization is feasible for a large class of practical examples, certainly including most hand-designed FSM's. We also present heuristic algorithms, that can handle large, machine-generated, FSM's. The possibly many different reduced machines with the same number of states have different implementation costs. We discuss two steps of the minimization procedure, called state mapping and solution shrinking, that have received little prior attention in the literature, though they play a significant role in delivering an optimally implemented reduced machine. We also introduce an algorithm whose main virtue is the ability to cope with very general cost functions, while Providing high performance.

关键词： heuristic algorithms Minimization methods Cost function Jacobian matrices Circuit synthesis System testing Central Processing Unit

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Evaluating Meta-heuristic algorithms for Dynamic Capacitated Arc Routing Problems Based on a Novel Lower Bound Method

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2024年第4期19卷 31-44页

作者： Tong, Hao Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham Birmingham England Honda Res Inst Europe GmbH Offenbach Germany Lingnan Univ Hong Kong Peoples R China

Meta-heuristic algorithms, especially evolutionary algorithms, have been frequently used to find near optimal solutions to combinatorial optimization problems. The evaluation of such algorithms is often conducted through comparisons with other algorithms on a set of benchmark problems. However, even if one algorithm is the best among all those compared, it still has difficulties in determining the true quality of the solutions found because the true optima are unknown, especially in dynamic environments. It would be desirable to evaluate algorithms not only relatively through comparisons with others, but also in absolute terms by estimating their quality compared to the true global optima. Unfortunately, true global optima are normally unknown or hard to find since the problems being addressed are NP-hard. In this paper, instead of using true global optima, lower bounds are derived to carry out an objective evaluation of the solution quality. In particular, the first approach capable of deriving a lower bound for dynamic capacitated arc routing problem (DCARP) instances is proposed, and two optimization algorithms for DCARP are evaluated based on such a lower bound approach. An effective graph pruning strategy is introduced to reduce the time complexity of our proposed approach. Our experiments demonstrate that our approach provides tight lower bounds for small DCARP instances. Two optimization algorithms are evaluated on a set of DCARP instances through the derived lower bounds in our experimental studies, and the results reveal that the algorithms still have room for improvement for large complex instances.

关键词： heuristic algorithms Metaheuristics Avalanche photodiodes Evolutionary computation Routing Approximation algorithms Vehicle dynamics Time complexity Performance evaluation Benchmark testing Optimization methods

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Evaluation of nine heuristic algorithms with data-intensive jobs and computing-intensive jobs in a dynamic environment

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IET SOFTWARE 2015年第1期9卷 7-16页

作者： Hao, Yongsheng Liu, Guanfeng Nanjing Univ Informat Sci & Technol Network Ctr Nanjing 210044 Jiangsu Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China

This study focuses on a dynamic environment where data-intensive jobs and computing-intensive jobs are submitted to a grid at the same time. The authors analyse nine heuristic algorithms in a grid and give a comparison of them in a simulation environment. The nine heuristics are: (i) min-min, (ii) max-min, (iii) duplex, (iv) sufferage, (v) minimum execution time (MET), (vi) opportunistic load balancing (OLB), (vii) fast-fit, (viii) best-fit and (ix) adaptive scoring job scheduling (ASJS). In the simulation, different ratios between the data-intensive jobs and computing-intensive jobs are used to investigate for the performance of the nine heuristics under different arrival rates. Five parameters are used to estimate the performance of those methods. Those parameters include average execution time, average waiting time, the number of finished jobs (FB), the sum of file size that has been submitted to the grid (SFS) and the total number of instructions of all finished jobs (SINI). Simulation results show that four out of the nine heuristics have relative good performance in the job scheduling in the grid systems. They are best-fit, MET, ASJS and OLB.

关键词： dynamic environment OLB algorithm fast-fit algorithm data-intensive jobs best-flt algorithm opportunistic load balancing algorithm MET algorithm min-min algorithm SFS parameter average execution time FB parameter duplex algorithm Operating systems SINI parameter minimax techniques resource allocation job scheduling sufferage algorithm Grid computing adaptive scoring job scheduling scheduling minimum execution time algorithm max-min algorithm heuristic algorithms ASJS algorithm computing-intensive jobs grid systems grid computing flnished jobs total instructions number sum of file size average waiting time

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Designing a new medicine supply chain network considering production technology policy using two novel heuristic algorithms

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RAIRO-OPERATIONS RESEARCH 2021年第2期55卷 1015-1042页

作者： Goodarzian, Fariba Hoseini-Nasab, Hassan Toloo, Mehdi Fakhrzad, Mohammad Bagher Sci Network Innovat & Res Excellence Machine Intelligence Res Labs MIR Labs 113rd St NWPOB 2259 Auburn WA 98071 USA Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Yazd Univ Dept Ind Engn Yazd Iran Tech Univ Ostrava Fac Econ Republ Dept Syst Engn Ostrava Czech Republic Sultan Qaboos Univ Coll Econ & Polit Sci Dept Operat Management & Business Stat Muscat Oman

The role of medicines in health systems is increasing day by day. The medicine supply chain is a part of the health system that if not properly addressed, the concept of health in that community is unlikely to experience significant growth. To fill gaps and available challenging in the medicine supply chain network (MSCN), in the present paper, efforts have been made to propose a location-production-distribution-transportation-inventory holding problem for a multi-echelon multi-product multi-period bi-objective MSCN network under production technology policy. To design the network, a mixed-integer linear programming (MILP) model capable of minimizing the total costs of the network and the total time the transportation is developed. As the developed model was NP-hard, several meta-heuristic algorithms are used and two heuristic algorithms, namely, Improved Ant Colony Optimization (IACO) and Improved Harmony Search (IHS) algorithms are developed to solve the MSCN model in different problems. Then, some experiments were designed and solved by an optimization solver called GAMS (CPLEX) and the presented algorithms to validate the model and effectiveness of the presented algorithms. Comparison of the provided results by the presented algorithms and the exact solution is indicative of the high-quality efficiency and performance of the proposed algorithm to find a near-optimal solution within reasonable computational time. Hence, the results are compared with commercial solvers (GAMS) with the suggested algorithms in the small-sized problems and then the results of the proposed meta-heuristic algorithms with the heuristic methods are compared with each other in the large-sized problems. To tune and control the parameters of the proposed algorithms, the Taguchi method is utilized. To validate the proposed algorithms and the MSCN model, assessment metrics are used and a few sensitivity analyses are stated, respectively. The results demonstrate the high quality of the propose

关键词： Medicine supply chain network production-distribution problem heuristic algorithms Taguchi method

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Benchmarking of heuristic algorithms for Energy Router-Based Packetized Energy Management in Smart Homes

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IEEE SYSTEMS JOURNAL 2023年第2期17卷 2721-2732页

作者： Hussain, Hafiz Majid Ahmad, Ashfaq Narayanan, Arun Nardelli, Pedro H. J. Yang, Yongheng Lappeenranta Univ Technol Dept Elect Engn Lappeenranta 53850 Finland Air Univ Dept Elect & Comp Engn Islamabad 44000 Pakistan Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

This article presents an ER-based PEM strategy for PV integrated smart homes to jointly optimize their load scheduling delays, energy transactions cost, and battery degradation cost. The proposed approach incorporates a MA case, where, the ER acts as a main selecting agent realized by all other system elements. This leads to a combinatorial optimization problem, which can be effectively solved by heuristic optimization methods (HOMs), namely, genetic algorithm (GA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and harmony search algorithm (HSA). Specifically, we investigate the impact of the hyperparameters of the HOMs on the designed ER-based PEM system. Simulations are carried out for multiple smart homes under varying weather conditions to evaluate the effectiveness of HOMs in terms of selected performance metrics. Results show that the ER-based PEM reduces the average aggregated system cost, ensures economic benefits by selling surplus energy, while meeting customers energy packet demand, satisfying their quality-of-service, and operational constraints.

关键词： Smart homes Costs Delays Biological system modeling Optimization heuristic algorithms Scheduling Energy Internet (EI) energy router (ER) heuristic algorithms packetized energy management systems (PEMs)

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Solving the constrained p-center problem using heuristic algorithms

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APPLIED SOFT COMPUTING 2011年第4期11卷 3321-3328页

作者： Davoodi, Mansoor Mohades, Ali Rezaei, Jafar Amirkabir Univ Technol Dept Math & Comp Sci Lab Algorithms & Computat Geometry Tehran Iran Delft Univ Technol Fac Technol Policy & Management Dept Technol Strategy & Entrepreneurship NL-2600 GA Delft Netherlands

The p-center problem is one of the location problems that have been studied in operations research and computational geometry. This paper describes a compatible discrete space version of the heuristic Voronoi diagram algorithm. Since the algorithm gets stuck in local optimums in some cases, we apply a number of changes in the body of the algorithm with regard to the geometry of the problem, in a way that it can reach the global optimum with a high probability. Finally, a comparison between the results of these two algorithms on several test problems and a real-world problem are presented. (C) 2011 Elsevier B.V. All rights reserved.

关键词： p-Center problem Voronoi diagram heuristic algorithms Optimization

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