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An Introductory Illustration to Hybrid Quantum-Inspired Metaheuristics
Published in Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves, Hybrid Quantum Metaheuristics, 2022
Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves
The metaheuristics as shown in point 1 is basically done by considering the origins of various algorithms. Representative of nature-inspired algorithms may include Fuzzy Systems (FS), Artificial Neural Networks (ANN), Evolutionary Algorithm (EA), and Swarm Optimization (SO), etc. So far, these algorithms have successfully been applied for solving different real-world problems. Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) [9] belongs to SO, whereas Genetic Algorithm (GA) and Differential Evolution (DE) are two examples of EA. Some popular non-nature-inspired metaheuristics are known to be Tabu Search (TS) and Iterated Local Search (ILS). The metaheuristics are often classified based on the memory utilization, i.e., whether they deploy memory or not. Memory-based metaheuristics as referred to point 2 generally use memory at every iterations and save the search history. On the contrary, memory-less algorithms perform other tasks, for example, sometimes they might accomplish a Markov process, and based on the information gained, the next course of action is determined in the search process. Lastly, metaheuristics of point 3 are classified into two types, called a single point-based metaheuristics and population-based metaheuristics.
Introduction to Coverage Optimization in Wireless Sensor Networks
Published in Huynh Thi Thanh Binh, Nilanjan Dey, Soft Computing in Wireless Sensor Networks, 2018
Huynh Thi Thanh Binh, Nguyen Hai Nam
From the previous discussion, the most challenges in WSN systems are formulated as combinatorial optimization problems and are classified as NP-hard problems (Bang Wang, 2011; Anju and Rishi, 2015). There exist two approaches to solve NP-hard problems: exact solutions and approximate solutions. The first approach includes branch and bound, branch and cut, branch and price, dynamic programming, linear and integer programming, Lagrange relaxation and so on. Meanwhile, the second approach consists of simulated annealing, tabu search, iterated local search, variable neighborhood search, genetic algorithm, memetic algorithm, etc.
Methodology
Published in Tolga Bektaş, Freight Transport and Distribution, 2017
The iterated local search (ILS) algorithm relies on a simple yet effective idea of repeatedly using local search, and perturbing a locally optimum solution when identified at each run of the local search. The perturbation operator is random, and it changes only a part of the locally optimum solution to move the search to other parts of the solution space.
A sustainable supply chain coordination model with an investment in green technology and electric equipment
Published in International Journal of Systems Science: Operations & Logistics, 2023
Ivan Darma Wangsa, Iwan Vanany, Nurhadi Siswanto
Studies on EV models are extensive but are limited to vehicle routing problems or routing optimisation. Schneider et al. (2014) examined EV routing problems with time windows and recharging stations (E-VRPTW). The authors proposed using the particle swarm optimisation (PSO) algorithm to find an optimal solution. They incorporated the best possibility of recharging at the available stations. Battery swap and its suggested location are provided by considering the capacitated vehicle routing problem (C-VRP) was investigated by Yang and Sun (2015). Montoya et al. (2017) examined E-VRP, whose battery-charge level is nonlinear charging functions (E-VRP-NL). The model was solved using a hybrid metaheuristic – combining an iterated local search (ILS) and a heuristic contraction (HC). Koç et al. (2019) extended the research by Montoya et al. (2017) and introduced an EV routing problem-solution with shared charging stations by considering the nonlinear battery charging functions. It minimised the charging station's establishment cost and overall transportation operational cost. The model was built by using mixed-integer linear programming.
A bi-objective optimization approach for configuring surgical trays with ergonomic risk consideration
Published in IISE Transactions on Healthcare Systems Engineering, 2019
Ehsan Ahmadi, Dale T. Masel, Diana Schwerha, Seth Hostetler
Solving the two previously mentioned sub-problems provides a good solution to the original TOP problem. However, as we discussed in section 4.2.1, generating the procedure-instrument incidence matrix involves randomly distributing elements within the blocks. This might lead to obtaining a solution far away from the global optimum solution. Therefore, we have designed an iterated local search (ILS) meta-heuristic to systematically search the solution space and move toward the global optimum solution. The ILS is a conceptually simple algorithm with easy implementation. The essence of the ILS is to iteratively apply a local search with a perturbation mechanism on a currently obtained solution for further exploration of the solution space. More specifically, the ILS consists of four components: initialization, perturbation, local search and acceptance criteria (Lourenço et al., 2003). Each of these components has been described in more depth in the following subsections.
Analytics and machine learning in vehicle routing research
Published in International Journal of Production Research, 2023
Ruibin Bai, Xinan Chen, Zhi-Long Chen, Tianxiang Cui, Shuhui Gong, Wentao He, Xiaoping Jiang, Huan Jin, Jiahuan Jin, Graham Kendall, Jiawei Li, Zheng Lu, Jianfeng Ren, Paul Weng, Ning Xue, Huayan Zhang
Considering that the VRP and its variants belong to the NP-hard class of problems, exact algorithms are only applicable under certain circumstances and for small-scale problems. Exact methods treat VRP as integer or mixed-integer programs, and try to find a (near-)optimal solution. Since real-life VRPs are usually of large sizes, heuristic-based methods are considered more suitable. Elshaer and Awad (2020) states that more than 70% of solution methods in the literature are based on metaheuristics, which have various ‘meta’ strategies capable of escaping from poor local optima but cannot guarantee optimality (Boussaïd, Lepagnot, and Siarry 2013). Representative exact algorithms for VRP include branch-and-price algorithm (Christiansen and Lysgaard 2007) and branch-and-cut algorithms (Augerat et al. 1995; Ralphs et al. 2003; Baldacci, Hadjiconstantinou, and Mingozzi 2004). Metaheuristics can be divided into two categories: single-point based heuristics, and population-based heuristics. The former includes classical heuristic methods, such as simulated annealing (SA) (Kirkpatrick, Gelatt, and Vecchi 1983), tabu search (TS) (Glover and Laguna 1997), GRASP (for greedy randomised adaptive search procedure) (Feo and Resende 1995), variable neighbourhood search (VNS) (Mladenović and Hansen 1997), guided local search (GLS) (Voudouris and Tsang 2003), iterated Local Search (ILS) (Stützle 1999), large neighbourhood search (LNS), and adaptive large neighbourhood search (ALNS) (Pisinger and Ropke 2010). The latter includes two main types, evolutionary algorithms and swarm intelligence, which are mainly inspired by some natural phenomena. The readers may refer to Elshaer and Awad (2020) for a comprehensive review on metaheuristic for VRP.