Hybrid meta-heuristic algorithms for solving network design problem

The formulations of the user-optimal dynamic traffic assignment (UODTA)-based network design problem (NDP), signal setting design problem (SSD), and the integrated NDP and SSD problem (NDP-SSD) for the deterministic (DET), stochastic (SP) and robust (RO) optimizations are reviewed. The modifications of the simulated annealing (SA), genetic algorithm (GA) and reactive tabu search (RTS) for the nine problems (DET/SP/RO of NDP/SSD/NDP-SSD) are discussed. In the experiment, SA, GA and RTS are compared for the nine problems to identify best algorithms. The best algorithm for each problem is employed to find a best solution, which is then evaluated under stochastic condition. We find that the RO solutions yield the best robustness and lowest risk, whereas the DET solutions perform worst. The integrated robust approach is the most desirable, and the next best approach is the sequential robust approach. The deterministic approach can yield worse solutions than the do-nothing case.

2차원 저장소 적재는 NP-hard 문제로서 그 문제의 정확한 해를 구하는 것이 어려운 것으로 알려져 있으며, 이의 더 좋은 해를 얻기 위해 유전자(genetic) 알고리즘, 시뮬레이티드 어닐링(simulated annealing), 타부서치(tabu search)등과 같은 근사적 접근법이 제안되어 왔다. 하지만 분지한계(branch-and-bound)나 타부서치 기법들을 이용한 기존의 대표적인 근사 알고리즘들은 휴리스틱 알고리즘의 해에 기반을 둠으로 효율성이 낮고 반복수행에 의한 계산시간이 길다. 따라서 본 논문에서는 이러한 근사 알고리즘의 복잡성을 간소화하고, 알고리즘의 효율성을 높이기 위해 적재가능성을 판단하는 적합성 함수(fitness function)를 정의하고 이를 이용하여 어떤 특정 개체의 적재영역을 판단하는데 영향을 주는 적재영역의 수를 계산한다. 또한, 이들을 이용한 새로운 휴리스틱 알고리즘을 제안하였다. 끝으로 기존의 휴리스틱 또는 메타휴리스틱 기법과의 비교실험을 통해 기존의 휴리스틱 알고리즘인 FFF와 FBS에 비해 97%의 결과가 같거나 우수하였으며, 타부서치 알고리즘에 비해 86%의 결과가 같거나 우수한 것으로 나타났다. The two-dimensional bin packing problem(2D-BPP) has been known to be NP-hard, and it is difficult to solve the problem exactly. Many approximation methods, such as genetic algorithm, simulated annealing and tabu search etc, have been also proposed to gain better solutions. However, the existing approximation algorithms, such as branch-and-bound and tabu search, have shown the low efficiency and the long execution time due to a large of iterations. To solve these problems, we first define the fitness function to simplify and increase the utility of algorithm. The function decides whether an item is packed into a given area, and as an important information for a packing strategy, the number of subarea that can accommodate a given item is obtained from the variant of the fitness function. Then we present a heuristic algorithm BF for 2D bin packing, constructed by the fitness function and subarea. Finally, the effectiveness of the proposed algorithm will be expressed by the comparison experiments with the heuristic and the metaheuristic of the literatures. As comparing with existing heuristic algorithms and metaheuristic algorithms, it has been found that the packing rate of algorithm BP is the same as 97% as existing heuristic algorithms, FFF and FBS, or better than them. Also, it has been shown the same as 86% as tabu search algorithm or better.

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.

Solving a dynamic cell formation problem using metaheuristics

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, 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.

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

An ant colony approach for clustering

The paper discusses a hypergraph-based polynomial time algorithm for constructing a DNA sequence corresponding to a given spectrum with no errors. The presence of n-ary relations in the expression of DNA sequencing led to the advent of hypergraphs in DNA sequencing. An efficient algorithm, unimodular hypergraph (UMHG), has been proposed for constructing unimodular hypergraph that represents the short-read DNA sequence of the given spectrum. The performance of the proposed algorithm is evaluated in comparison with prominent algorithms such as greedy, greedy (lag), ant colony optimization(ACO), multi-level ACO, enhanced genetic algorithm (GA), hybrid GA and tabu search against 40 instances. The proposed UMHG algorithm is found to outperform the other algorithms in terms of average similarity score. UMHG is significant in terms of minimum computing time, especially as the spectrum size increases computing time decreases considerably, owing to the unimodularity property.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Ant Colony Optimization (ACO) has been successfully employed to tackle a variety of hard combinatorial optimization problems, including the traveling salesman problem, vehicle routing, sequential ordering and timetabling. ACO, as a swarm intelligence framework, mimics the indirect communication strategy employed by real ants mediated by pheromone trails. Among the several algorithms following the ACO general framework, the Ant Colony System (ACS) has obtained convincing results in a range of problems. In Software Engineering, the effective application of ACO has been very narrow, being restricted to a few sparse problems. This paper expands this applicability, by adapting the ACS algorithm to solve the well-known Software Release Planning problem in the presence of dependent requirements. The evaluation of the proposed approach is performed over 72 synthetic datasets and considered, besides ACO, the Genetic Algorithm and Simulated Annealing. Results are consistent to show the ability of the proposed ACO algorithm to generate more accurate solutions to the Software Release Planning problem when compared to Genetic Algorithm and Simulated Annealing.

With the growth of environmental awareness, remanufacturing and sustainable manufacturing have become hot issues. Disassembly is the first step and critical activity in remanufacturing. Traditional disassembly sequence planning (DSP) focusses on sequential disassembly. However, it is inefficient for complicated products because only one manipulator is employed to execute disassembly operations. Thus, this work focusses on parallel DSP (PDSP) and proposes a selective parallel disassembly sequence planning (SPDSP) methodology, which performs disassembly compared to sequential DSP and PDSP. In this paper, a mathematical model is used to describe the constraint and precedence relationships, and a parallel sequence model is designed for parallel disassembly. A novel hybrid genetic algorithm (NHGA) based-multi-objective model of SPDSP is proposed for optimisation. In this model, two indicators are integrated: disassembly time (including basic disassembly time, tool exchange time and direction change time) and disassembly costs. A transmission box is used as an instance, and a comparison with conventional genetic algorithm (GA), simulated annealing (SA) and tabu search (TS) is made to validate the practicality of the proposed methodology.

The Hormesis-Based Optimization (HBO) algorithm is a new method designed to achieve optimization while keeping computational cost and time low. It can be applied to areas such as workload management, resource planning, and task scheduling. This method works by converting the system’s performance parameters into a single value termed as “stress”. It is inspired by the phenomenon of hormesis, where small doses of stress strengthen biological systems, and ensures a dynamic distribution of stress to optimize system performance. The key strength of HBO lies in its ability to quickly and effectively decide the optimal adjustment of this ‘stress’ across various components, which is critical for achieving the best possible performance and can be a NP-hard problem in multi-constrained system. To test this principle, we applied the HBO algorithm to the system of machines, tasked with predictive-reactive dynamic job shop scheduling (DJSS), with the aim of reducing the overall job completion time and latency of the total number of jobs. The results show that the HBO algorithm outperformed not only conventional techniques like Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS), but also their adaptive methods such as Adaptive Genetic Algorithm (AGA), Adaptive Simulated Annealing (ASA), and Adaptive Tabu Search (ATS). Specifically, it improved the total job completion time (makespan) by an average of 4.15% and reduced latency by 4.79%, with time complexity of O(T∙n∙log⁡〖(n))〗, as compared to best performing ATS technique which has time complexity of O(n^2 ) for the worst case.

Three heuristic search methods, Genetic Algorithm, Simulated Annealing and Tabu Search are implemented to invert Rayleigh wave phase velocity for shallow S-wave velocity profiling in seismic surface wave surveying. Unlike linearized least-squares inversion, they do not require derivative calculation, or an initial model, only the forward modeling calculation. In this study, the performances of the three heuristic techniques are compared with numerical experiments. With common paramerization and search limits, the three algorithms can accurately reconstruct shallow S-wave profiles with and without a velocity reversal from the synthetic data without any specific initial models. The Genetic Algorithms and the Simulated Annealing show fast convergence. However, the Simulated Annealing can find models with the smallest misfits. It is also found that the decrease of the misfits is the most gradual in the Tabu Search.

Logistics is vital to sustaining many industrial, commercial, and administrative activities. It is often composed of the logistics service providers and the customers being serviced. The goal of service providers is to maximize revenues by servicing customers efficiently within their preferred timelines. To achieve this goal, they are often involved in activities of location-allocation planning, that is, which logistics facilities be opened, where they should be opened, and how customer allocations should be performed to ensure timely service to customers at least delivery costs to logistics operators. Location-allocation problem is NP-hard. In literature, metaheuristics have been shown to perform better than exact programming approaches to tackle larger NP-hard problems. We present four metaheuristics based solution approaches namely Genetic algorithms (GA), Simulated annealing (SA), Tabu search (TS), and Ant colony optimization (ACO) to address the capacitated location allocation problem on logistics networks. The problem is studied under two cases. In the first case, opening costs of the facilities and only one criterion (distance) is used. In the second case, opening costs of the facilities and multiple criteria (distance, travel cost, travel time) are used. The proposed approaches are tested under various problem instances to verify and validate the model results.