A hybrid approach for data clustering based on modified cohort intelligence and K-means

Clustering is an important and popular technique in data mining. It partitions a set of objects in such a manner that objects in the same clusters are more similar to each another than objects in the different cluster according to certain predefined criteria. K-means is simple yet an efficient method used in data clustering. However, K-means has a tendency to converge to local optima and depends on initial value of cluster centers. In the past, many heuristic algorithms have been introduced to overcome this local optima problem. Nevertheless, these algorithms too suffer several short-comings. In this paper, we present an efficient hybrid evolutionary data clustering algorithm referred to as K-MCI, whereby, we combine K-means with modified cohort intelligence. Our proposed algorithm is tested on several standard data sets from UCI Machine Learning Repository and its performance is compared with other well-known algorithms such as K-means, K-means++, cohort intelligence (CI), modified cohort intelligence (MCI), genetic algorithm (GA), simulated annealing (SA), tabu search (TS), ant colony optimization (ACO), honey bee mating optimization (HBMO) and particle swarm optimization (PSO). The simulation results are very promising in the terms of quality of solution and convergence speed of algorithm.     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.     

Metaheuristic methods in hybrid flow shop scheduling problem

Memetic algorithms are hybrid evolutionary algorithms that combine global and local search by using an evolutionary algorithm to perform exploration while the local search method performs exploitation. This paper presents two hybrid heuristic algorithms that combine particle swarm optimization (PSO) with simulated annealing (SA) and tabu search (TS), respectively. The hybrid algorithms were applied on the hybrid flow shop scheduling problem. Experimental results reveal that these memetic techniques can effectively produce improved solutions over conventional methods with faster convergence.     

Multi-objective optimization with fuzzy measures and its application to flow-shop scheduling

Most of the research in multi-objective scheduling optimization uses the classical weighted arithmetic mean operator to aggregate the various optimization criteria. However, there are scheduling problems where criteria are considered interact and thus a different operator should be adopted. This paper is devoted to the search of Pareto-optimal solutions in a tri-criterion flow-shop scheduling problem (FSSP) considering the interactions among the objectives. A new hybrid meta-heuristic is proposed to solve the problem which combines a genetic algorithm (GA) for solutions evolution and a reduced variable neighborhood search (RVNS) technique for fast solution improvement. To deal with the interactions among the three criteria the discrete Choquet integral method is adopted as a means to aggregate the criteria in the fitness function of each individual solution. Experimental comparisons (over public available FSSP test instances) with five existing multi-objective evolutionary algorithms (including the well known SPEA2 and NSGAII algorithms as well as the recently published L-NSGA algorithm) showed a superior performance for the developed approach in terms of diversity and domination of solutions.     

Data Generation for Path Testing

We present two stochastic search algorithms for generating test cases that execute specified paths in a program. The two algorithms are: a simulated annealing algorithm (SA), and a genetic algorithm (GA). These algorithms are based on an optimization formulation of the path testing problem which include both integer- and real-value test cases. We empirically compare the SA and GA algorithms with each other and with a hill-climbing algorithm, Korel's algorithm (KA), for integer-value-input subject programs and compare SA and GA with each other on real-value subject programs. Our empirical work uses several subject programs with a number of paths. The results show that: (a) SA and GA are superior to KA in the number of executed paths, (b) SA tends to perform slightly better than GA in terms of the number of executed paths, and (c) GA is faster than SA; however, KA, when it succeeds in finding the solution, is the fastest.     

Development,application, and comparison of hybrid meta-heuristics for urban land-use allocation optimization: Tabu search,genetic, GRASP,and simulated annealing algorithms

Land-use optimization problem (LUOP) that seeks to allocate different land types to land units involves various dimensions and deals with numerous conflicting objectives and a large set of data and variables. Single meta-heuristics are broadly developed and applied for solving LUOP. Despite the acceptable solutions derived from these algorithms, researchers in both academic and practical areas face the interesting question: can we develop an algorithm with better efficiency and solution quality? In the literature of operation research, hybridization, a combination of meta-heuristics, was introduced as a way of generating better algorithms. Therefore, this paper aims at developing novel algorithms through hybridizing Tabu search (TS), genetic algorithm (GA), GRASP, and simulated annealing (SA) and examining their quality and efficiency in practice. Accordingly, low-level teamwork GRASP–GA–TS (LLTGRGATS), high-level relay Greedy–GA–TS, and high-level teamwork SA were developed. Firstly, these algorithms were applied for solving small- and large-size single-row facility layout problem to evaluate their performance and functionality and to select the satisfactory algorithm in comparison with the other developed hybrids. Secondly, the selected algorithm, LLTGRGATS, and SVNS, a recent hybrid algorithm proposed for solving LUOP, were performed on a study area to solve a LUOP with two constraints and seven nonlinear objective functions. The outputs showed that the quality and efficiency of LLTGRGATS were slightly better than those of SVNS and it can be considered as a favorable tool for land-use planning process.     

Application of heuristic and hybrid-GASA algorithms to tool-path optimization problem for minimizing airtime during machining

In this paper, heuristic algorithms such as simulated annealing (SA), genetic algorithm (GA) and hybrid algorithm (hybrid-GASA) were applied to tool-path optimization problem for minimizing airtime during machining. Many forms of SA rely on random starting points that often give poor solutions. The problem of how to efficiently provide good initial estimates of solution sets automatically is still an ongoing research topic. This paper proposes a hybrid approach in which GA provides a good initial solution for SA runs. These three algorithms were tested on three-axis-cartesian robot during milling of wood materials. Their performances were compared based on minimum path and consequently minimum airtime. In order to make a comparison between these algorithms, two cases among the several milling operations were given here. According to results obtained from these examples, hybrid algorithm gives better results than other heuristic algorithms alone. Due to combined global search feature of GA and local search feature of SA, hybrid approach using GA and SA produces about 1.5% better minimum path solutions than standard GA and 47% better minimum path solutions than standard SA.     

Evolutionary algorithms for scheduling m-machine flow shop with lot streaming

This paper addresses the problem of making sequencing and scheduling decisions for n jobs–m-machines flow shops under lot sizing environment. Lot streaming (Lot sizing) is the process of creating sub lots to move the completed portion of a production sub lots to down stream machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. Evolutionary algorithms that belong to search heuristics find more applications in recent research. Genetic algorithm (GA) and hybrid genetic algorithm (HEA) also known as hybrid evolutionary algorithm fall under evolutionary heuristics. On this concern this paper proposes two evolutionary algorithms namely, GA and HEA to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set-up time. The following two algorithms are used to evaluate the performance of the proposed GA and HEA: (i) Baker's algorithm (BA), an optimal solution procedure for two-machine flow shop problem with lot streaming and makespan objective criterion and (ii) simulated annealing algorithm (SA) for m-machine flow shop problem with lot streaming and makespan and total flow time criteria.     

Particle swarm optimization with crazy particles for nonconvex economic dispatch

The paper presents an effective evolutionary method for economic power dispatch. The idea is to allocate power demand to the on-line power generators in such a manner that the cost of operation is minimized. Conventional methods assume quadratic or piecewise quadratic cost curves of power generators but modern generating units have non-linearities which make this assumption inaccurate. Evolutionary optimization methods such as genetic algorithms (GA) and particle swarm optimization (PSO) are free from convexity assumptions and succeed in achieving near global solutions due to their excellent parallel search capability. But these methods usually tend to converge prematurely to a local minimum solution, particularly when the search space is irregular. To tackle this problem “crazy particles” are introduced and their velocities are randomized to maintain momentum in the search and avoid saturation. The performance of the PSO with crazy particles has been tested on two model test systems, compared with GA and classical PSO and found to be superior.     

A tabu search method for geometric primitive extraction

In this paper, we propose a novel method for extracting the geometric primitives from geometric data, which is essentially an optimization problem. Specifically, we use tabu search to solve geometric primitive extraction problem. To the best of our knowledge, it is the first attempt that tabu search is used in computer vision. Our tabu search (TS) has a number of advantages: (1) TS avoids entrapment in local minima and continues the search to give a near-optimal final solution; (2) TS is very general and conceptually much simpler than either simulated annealing (SA) or genetic algorithm (GA); (3) TS has no special space requirement and is very easy to implement (the entire procedure only occupies a few lines of code); (4) our TS-based method can successfully extract some geometric primitives which are specially difficult for the traditional methods such as Hough Transform (HT) and Robust Statistics (RS). TS is a flexible framework of a variety of strategies originating from artificial intelligence and is therefore open to further improvement.     

An evolutionary tabu search for cell image segmentation

Many engineering problems can be formulated as optimization problems. It has become more and more important to develop an efficient global optimization technique for solving these problems. In this paper, we propose an evolutionary tabu search (ETS) for cell image segmentation. The advantages of genetic algorithms (GA) and TS algorithms are incorporated into the proposed method. More precisely, we incorporate "the survival of the fittest" from evolutionary algorithms into TS. The method has been applied to the segmentation of several kinds of cell images. The experimental results show that the new algorithm is a practical and effective one for global optimization; it can yield good, near-optimal solutions and has better convergence and robustness than other global optimization approaches.     

Metaheuristic approaches to grouping problems in high-throughput cryopreservation operations for fish sperm

High-throughput cryopreservation operations of fish sperm is a technology being developed by researchers today. This paper first formulates a grouping problem in high-throughput cryopreservation operations of fish sperm and then develops a heuristic and four metaheuristic algorithms for its solution. The heuristic is modified from one originally proposed for the assembly line balancing problem. The four metaheuristic algorithms include simulated annealing (SA), tabu search (TS), ant colony optimization (ACO), and a hybrid differential evolution (hDE). For each metaheuristic algorithm, four different initialization methods were used. For both SA and TS, five different neighborhood solution generation methods were also studied. Real world data collected from a high-throughput cryopreservation operation was used to test the effectiveness of algorithms with different initialization and neighborhood solution generation methods. For comparison, a base line of grouping by processing order was also established. The results indicate that: (i) all algorithms performed better than the base line; (ii) using the result of the modified heuristic as the initial solution of metaheuristic algorithms lead to a better solution; the amount of improvement varied from algorithm to algorithm; (iii) among the five neighborhood solution generation operators, insertion operator was the best; (iv) among all algorithms tested, the hybrid differential evolution is the best, followed by tabu search in terms of average objective value.     

基于混合策略的QoS选播流路由优化算法

分析了QoS选播流交错服务问题的产生过程及其对网络资源的浪费，提出了一种基于混合策略的全局路由优化解决方法．在构建路由优化问题模型的基础上，得出了选播流路由端到端时延、服务器负载、网络流量、路径调整等多个优化目标和QoS约束的表达式．给出了GA、SA、TS三种算法有机结合形成的一种混合优化算法，并说明了运用该算法求解的关键步骤和实现过程．实验结果表明，本算法能够在满足QoS约束的前提下实现选播路由的多目标组合优化，与基于GA或者SA的求解算法相比具有更强的稳定性和更高的精确度．     

A very fast TS/SA algorithm for the job shop scheduling problem

The job shop scheduling problem (JSP) is one of the most notoriously intractable NP-complete optimization problems. Over the last 10–15 years, tabu search (TS) has emerged as an effective algorithmic approach for the JSP. However, the quality of solutions found by tabu search approach depends on the initial solution. To overcome this problem and provide a robust and efficient methodology for the JSP, the heuristics search approach combining simulated annealing (SA) and TS strategy is developed. The main principle of this approach is that SA is used to find the elite solutions inside big valley (BV) so that TS can re-intensify search from the promising solutions. This hybrid algorithm is tested on the standard benchmark sets and compared with the other approaches. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times. For example, 17 new upper bounds among the unsolved problems are found in a short time.     

Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization

This work presents particle swarm optimization (PSO), a collaborative population-based meta-heuristic algorithm for solving the Cardinality Constraints Markowitz Portfolio Optimization problem (CCMPO problem). To our knowledge, an efficient algorithmic solution for this nonlinear mixed quadratic programming problem has not been proposed until now. Using heuristic algorithms in this case is imperative. To solve the CCMPO problem, the proposed improved PSO increases exploration in the initial search steps and improves convergence speed in the final search steps. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the proposed PSO is much more robust and effective than existing PSO algorithms, especially for low-risk investment portfolios. In most cases, the PSO outperformed genetic algorithm (GA), simulated annealing (SA), and tabu search (TS).     

Heuristic methods for vehicle routing problem with time windows

This paper documents our investigation into various heuristic methods to solve the vehicle routing problem with time windows (VRPTW) to near optimal solutions. The objective of the VRPTW is to serve a number of customers within predefined time windows at minimum cost (in terms of distance travelled), without violating the capacity and total trip time constraints for each vehicle. Combinatorial optimisation problems of this kind are non-polynomial-hard (NP-hard) and are best solved by heuristics. The heuristics we are exploring here are mainly third-generation artificial intelligent (AI) algorithms, namely simulated annealing (SA), Tabu search (TS) and genetic algorithm (GA). Based on the original SA theory proposed by Kirkpatrick and the work by Thangiah, we update the cooling scheme and develop a fast and efficient SA heuristic. One of the variants of Glover's TS, strict Tabu, is evaluated and first used for VRPTW, with the help of both recency and frequency measures. Our GA implementation, unlike Thangiah's genetic sectoring heuristic, uses intuitive integer string representation and incorporates several new crossover operations and other advanced techniques such as hybrid hill-climbing and adaptive mutation scheme. We applied each of the heuristics developed to Solomon's 56 VRPTW 100-customer instances, and yielded 18 solutions better than or equivalent to the best solution ever published for these problems. This paper is also among the first to document the implementation of all the three advanced AI methods for VRPTW, together with their comprehensive results.     

Simulated annealing algorithm for solving the single machine early/tardy problem

Recently, several studies have been conducted regarding the application of the simulated annealing (SA) method to solve combinatorial optimization problems. Most of the previous reports have shown that SA has been used to obtain reasonable solutions that are better than some heuristic algorithms and in comparable CPU time. A single machine early/tardy problem is selected in this paper to demonstrate the usefulness of SA. Firstly, based on previous studies, this research uses the factorial experiment to analyse the factors that are critical to the efficiency of SA. Secondly, SA, tuned by the previous step, is compared with branch-and-bound and neighbourhood search methods by solving some test problems. The results show that SA is very sensitive to the cooling schedule, generation mechanism, acceptance criteria and stopping criteria. When SA is used to solve the single machine problem with n ≤ 15, it can converge to the optimal solution quickly. For the single machine problem where n ≥ 30, the branch-and-bound algorithm is not feasible while SA can provide a much better solution than the neighbourhood search method.     

A simple and effective evolutionary algorithm for the vehicle routing problem

The vehicle routing problem (VRP) plays a central role in the optimization of distribution networks. Since some classical instances with 75 nodes resist the best exact solution methods, most researchers concentrate on metaheuristics for solving real-life problems. Contrary to the VRP with time windows, no genetic algorithm (GA) can compete with the powerful tabu search (TS) methods designed for the VRP. This paper bridges the gap by presenting a relatively simple but effective hybrid GA. In terms of average solution cost, this algorithm outperforms most published TS heuristics on the 14 classical Christofides instances and becomes the best solution method for the 20 large-scale instances generated by Golden et al.Scope and purposeThe framework of this research is the development of effective metaheuristics for hard combinatorial optimization problems met in vehicle routing. It is surprising to notice in the literature the absence of effective genetic algorithms (GA) for the vehicle routing problem (VRP, the main capacitated node routing problem), contrary to node routing problems with time windows or arc routing problems. Earlier attempts were based on chromosomes with trip delimiters and needed a repair procedure to get feasible children after each crossover. Such procedures are known to weaken the genetic transmission of information from parents to children. This paper proposes a GA without trip delimiters, hybridized with a local search procedure. At any time, a chromosome can be converted into an optimal VRP solution (subject to chromosome sequence), thanks to a special splitting procedure. This design choice avoids repair procedures and enables the use of classical crossovers like OX. The resulting algorithm is flexible, relatively simple, and very effective when applied to two sets of standard benchmark instances ranging from 50 to 483 customers.     

A comparison of scheduling algorithms for flexible flow shop problems with unrelated parallel machines,setup times,and dual criteria

This paper considers a flexible flow shop scheduling problem, where at least one production stage is made up of unrelated parallel machines. Moreover, sequence- and machine-dependent setup times are given. The objective is to find a schedule that minimizes a convex sum of makespan and the number of tardy jobs in a static flexible flow shop environment. For this problem, a 0–1 mixed integer program is formulated. The problem is, however, a combinatorial optimization problem which is too difficult to be solved optimally for large problem sizes, and hence heuristics are used to obtain good solutions in a reasonable time. The proposed constructive heuristics for sequencing the jobs start with the generation of the representatives of the operating time for each operation. Then some dispatching rules and flow shop makespan heuristics are developed. To improve the solutions obtained by the constructive algorithms, fast polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges of jobs are applied. In addition, metaheuristics are suggested, namely simulated annealing (SA), tabu search (TS) and genetic algorithms. The basic parameters of each metaheuristic are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages and with an optimal solution for small-size problems. We have found that among the constructive algorithms the insertion-based approach is superior to the others, whereas the proposed SA algorithms are better than TS and genetic algorithms among the iterative metaheuristic algorithms.     

A multi-objective mixed-model assembly line sequencing problem in order to minimize total costs in a Make-To-Order environment,considering order priority

Nowadays, mixed-model assembly line is used increasingly as a result of customers’ demand diversification. An important problem in this field is determining the sequence of products for entering the line. Before determining the best sequence of products, a new procedure is introduced to choose important orders for entering the shop floor. Thus the orders are sorted using an analytical hierarchy process (AHP) approach based on three criteria: critical ratio of each order (CR_o), Significance degree of customer and innovation in a product, while the last one is presented for the first time. In this research, six objective functions are presented: minimizing total utility work cost, total setup cost and total production rate variation cost are the objectives which were presented previously, another objective is minimizing total idle cost, meanwhile two other new objectives regarding minimizing total operator error cost and total tardiness cost are presented for the first time. The total tardiness cost tries to choose a sequence of products that minimizes the tardiness cost for customers with high priority. First, to check the feasibility of the model, GAMS software is used. In this case, GAMS software could not search all of the solution space, so it is tried in two stages and because this problem is NP-hard, particle swarm optimization (PSO) and simulated annealing (SA) algorithms are used. For small sized problems, to compare exact method with proposed algorithms, the problem must be solved using meta-heuristic algorithms in two stages as GAMS software, whereas for large sized problems, the problem can be solved in two ways (one stage and two stages) by using proposed algorithms; the computational results and pairwise comparisons (based on sign test) show GAMS is a proper software to solve small sized problems, whereas for a large sized problem the objective function is better when solved in one stage than two stages; therefore it is proposed to solve the problem in one stage for large sized problems. Also PSO algorithm is better than SA algorithm based on objective function and pairwise comparisons.