Comparative analysis of three metaheuristics for short-term open pit block sequencing

Researchers have indicated that a permutation schedule can be improved by a non-permutation schedule in a flowshop with completion time-based criteria, such as makespan and total completion time. This study proposes a hybrid approach which draws on the advantages of simulated annealing and tabu search for the non-permutation flowshop scheduling problem, in which the objective function is the makespan of the schedule. To verify the effectiveness of the proposed hybrid approach, computational experiments are performed on a set of well-known non-permutation flowshop scheduling benchmark problems. The result shows that the performance of the hybrid approach is better than that of other approaches, including ant colony optimisation, simulated annealing, and tabu search. Further, the proposed approach found new upper bound values for all benchmark problems within a reasonable computational time.

This paper presents a real-life variable periodic vehicle routing problem (VPVRP) which is originated from an auto parts company. The idea of lean production has been applied in the production of the auto parts manufacturer. The VPVRP consists of the assignment of a pickup schedule for each of its supplier’s locations and also the establishment of daily routes in order to minimize total transportation cost. Since the problem is a special case of the periodic vehicle routing problem, several initial solution algorithms and a hybrid tabu search (HTS) are developed for solving the problem. There are distinctive features in the HTS, including the intensification strategy, the diversification strategy, and the combination of tabu search with the switch mechanism. The computational results show that the proposed initial algorithms and the HTS outperform the current scheduling method used by the case plant with a significant improvement.

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.

In this paper, hybrid tabu search and simulated annealing (TSSA) with search space managing methods are proposed to determine the optimal number and allocation of FACTS controllers to enhance power transfer capability of power transactions between generators and loads in power systems. Particular optimal allocation includes optimal locations and parameter settings. Two types of FACTS controllers including thyristor-controlled series capacitor (TCSC) and static var compensator (SVC) are used individually. The objective function is formulated as maximizing total transfer capability (TTC) and minimizing power losses. Power transfer capability determinations are calculated based on the optimal power flow (OPF) technique. Split and non-split search space managing methods are used to improve the solution searching capability. Test results on IEEE 118-bus system and the practical Electricity Generating Authority of Thailand (EGAT) 58-bus system show that the proposed hybrid TSSA with optimal number of FACTS criteria and the split search space managing method give higher TTC and less number of FACTS controllers than those from evolutionary programming (EP) and non-split search space method.

In this paper, a hybrid tabu search and simulated annealing (TS/SA) approach is proposed to minimize the generator fuel cost in optimal power flow (OPF) control with multi-type of flexible AC transmission systems (FACTS) devices. The problem is decomposed into the optimal setting of FACTS parameters subproblem that is searched by the hybrid TS/SA approach and the OPF with fixed FACTS parameters subproblem that is solved by the quadratic programming (QP). Four types of FACTS devices are used: thyristor-controlled series capacitor (TCSC), thyristor-controlled phase shifter (TCPS), unified power flow controller (UPFC), and static VAr compensator (SVC). Test results on the modified IEEE 30 bus system with multi-type of FACTS devices indicate that the proposed hybrid TS/SA approach can obtain better solutions and require less CPU times than genetic algorithm, SA, or TS alone. Using multi-type of FACTS devices results in less total generator fuel cost than using individual FACTS device.

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Hybridization of tabu search with feasible and infeasible local searches for periodic home health care logistics

Supervised Neural Network (NN) learning is a process in which input patterns and known targets are presented to a NN while it learns to recognize (classify, map, fit, etc.) them as desired. The learning is mathematically defined as an optimisation problem, i.e., an error function representing the differences between the desired and actual output, is being minimized (Bishop, 1995; Haykin, 1999). Because the most popular supervised learning techniques are gradient based (Backpropagation BP), they suffer from the so-called Local Minima Problem (Bishop, 1995). This has motivated the employment of Global Optimisation (GO) methods for the supervised NN learning. Stochastic and heuristic GO approaches including Evolutionary Algorithms (EA) demonstrated promising performance over the last decades (Smagt, 1994; Sexton et al., 1998; Jordanov & Georgieva, 2007; etc.). EA appeared more powerful than BP and its modifications (Sexton et al., 1998; Alba & Chicone 2004), but hybrid methods that combine the advantages of one or more GO techniques and local searches were proven to be even better (Yao, 1999; Rocha et al., 2003; Alba & Chicano, 2004; Ludemir et al., 2006). Hybrid methods were promoted over local searches and simple population based techniques in Alba & Chicone (2004). The authors compared five methods: two BP implementations (gradient descent and Levenberg-Marquardt), Genetic Algorithms (GA), and two hybrid methods, combining GA with different local methods. The methods were used for NN learning applied to problems arising in medicine. Ludemir et al. (2006) optimized simultaneously NN weights and topology with a hybrid method combining Simulated Annealing (SA), Tabu Search (TS) and BP. A set of new solutions was generated on each iteration by TS rules, but the best solution was only accepted according to the probability distribution as in conventional SA. Meanwhile, the topology of the NN was also optimized and the best solution was kept. Finally, BP was used to train the best NN topology found in the previous stages. The new methodology compared favorably with SA and TS on four classification and one prediction problems. Plaginakos et al. (2001) performed several experiments to evaluate various training methods – six Differential Evolution (DE) implementations (with different mutation operators), BP, BPD (BP with deflection), SA, hybridization of BP and SA (BPSA), and GA. They reported O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

In this paper, we address the flowpath design issue of Automated Guided Vehicle Systems (AGVSs). In particular, we concentrate on the design of unidirectional flowpaths (i.e. vehicles are restricted to travel only in one direction along a given segment of the flowpath). We have developed intelligent heuristics — simulated annealing and tabu search algorithms for the design of unidirectional AGVSs. Different versions of simulated annealing and tabu search algorithms are implemented. Our extensive computational results indicate that both simulated annealing and tabu search yield solutions of adequate quality for all practical purposes. A tabu search implementation with the use of a frequency-based memory structure dominates all tested heuristics in terms of solution quality (i.e. percent deviation from optimality), with an impressive average performance over 45 test problems of less than 0.85% deviation from optimality.

Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem

In order to promote the cost reduction and efficiency improvement of the logistics distribution process and to guarantee the safety of goods transportation, this paper studies the portfolio optimization of goods loading and the problem of simultaneous pickup and delivery vehicle routing. A balanced loading constraint was introduced to restrict loading through two aspects of axle weight bearing and lateral center-of-gravity offset. With the shortest total route length as the objective, this paper constructs a simultaneous pickup and delivery vehicle routing model with three-dimensional (3D) balanced loading constraints (3BL-VRPSPD). Additionally, a hybrid tabu search (TS) algorithm embedded loading test was proposed to solve this problem. Firstly, a heuristic insertion method was applied to determine the initial routing scheme, and the node swapping and relocation operators were designed to construct the tabu neighborhood scheme for routing optimization. On this basis, the 3D balanced loading was incorporated into the routing iteration process. A balanced loading algorithm, combining multiple-indicator ordering and maximum space division strategies (MOMD), was formulated to develop a 3D-balanced loading plan for goods with a pickup and delivery vehicle routing scheme. Finally, standard instances verified the effectiveness of the method. The results show that the proposed method can effectively optimize 3BL-VRPSPD and outperform other algorithms.

Optimization methods are critical to radiation therapy. A new technology, quantum annealing (QA), employs novel hardware and software techniques to address various discrete optimization problems in many fields. We report on the first application of quantum annealing to the process of beamlet intensity optimization for IMRT.We apply recently-developed hardware which natively exploits quantum mechanical effects for improved optimization. The new algorithm, called QA, is most similar to simulated annealing, but relies on natural processes to directly minimize a system’s free energy. A simple quantum system is slowly evolved into a classical system representing the objective function. If the evolution is sufficiently slow, there are probabilistic guarantees that a global minimum will be located.To apply QA to IMRT-type optimization, two prostate cases were considered. A reduced number of beamlets were employed, due to the current QA hardware limitations. The beamlet dose matrices were computed using CERR and an objective function was defined based on typical clinical constraints, including dose-volume objectives, which result in a complex non-convex search space. The objective function was discretized and the QA method was compared to two standard optimization methods, simulated annealing and Tabu search, run on a conventional computing cluster.Based on several runs, the average final objective function value achieved by the QA was 16.9 for the first patient, compared with 10.0 for Tabu and 6.7 for the simulated annealing (SA) method. For the second patient, the values were 70.7 for the QA, 120.0 for Tabu and 22.9 for the SA. The QA algorithm required 27–38% of the time required by the other two methods.In this first application of hardware-enabled QA to IMRT optimization, its performance is comparable to Tabu search, but less effective than the SA in terms of final objective function values. However, its speed was 3–4 times faster than the other two methods. This initial experiment suggests that more research into QA-based heuristics may offer significant speedup over conventional clinical optimization methods, as quantum annealing hardware scales to larger sizes.

An exact optimization approach for an integrated process configuration, lot-sizing, and scheduling problem

In this paper, we study the resource management problem in Direct Sequence-Wideband Code Division Multiple Access (DS-WCDMA) systems. The control variables, transmission power, and transmission rate for resource management are considered. Three meta-heuristic techniques, Genetic Algorithms (GA), Simulated Annealing (SA), and Tabu Search (TS), are utilized to solve this optimization problem. Also, a nonlinear programming technique, Generalized Reduced Gradient (GRG) method, is adopted to compare with the three meta-heuristic techniques. Two approaches, the single objective approach and the multiobjective approach, are used in the simulation. The results obtained by GA, SA, TS, and GRG are compared in a single objective approach. In a multiobjective approach, Multiobjective Genetic Algorithm (MOGA) is employed to compare with the other two well-known multiobjective evolutionary algorithms (MOEAs), Pareto Archived Evolution Strategy (PAES) and Micro-Genetic Algorithms (MICRO-GA). Two scenarios, scenario (a): 25 users and scenario (b): 50 users, are considered for both approaches. The simulation results of a single objective approach show that GA outperforms SA, TS, and GRG in the two scenarios. Also, the simulation results of a multiobjective approach show that MOGA outperforms PAES and MICRO-GA, and obtains nondominated trade-off solutions with better convergence and diversity between trade minimization of total power and maximization of total rate in two scenarios.

Evolutionary algorithms, simulated annealing and tabu search: a comparative study