Effective Search Algorithm Research Articles

Regional Multicriteria and Multimodal Route Planning System for Public Transportation: A Case Study

Nowadays, the use of computer-based route planners is popular among private and public transportation passengers. A large range of websites and GPS navigation devices provide such services for their users. Here, we present an algorithm used by a route planning system which operates on a complete public transport network of two regions from two countries, namely Hungary and Serbia. The algorithm can handle the pedestrian traffic between stops not too far from each other. It can take into account individual user preferences like walking distances and modes of transport. The graph representing the transport network was very large, but with the help of some speed-up techniques, we managed to create an effective search algorithm that is able to handle user requirements.

An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks

Gravitational search algorithm (GSA) is a recently introduced meta-heuristic that has shown great performance in numerical function optimization and solving real world problems. GSA provides an excellent social interaction between its search agents. This social interaction results in admirable exploration of the search space and gives a unique social component to GSA. However, the social interaction is not able to exploit good solutions in an efficient manner. To overcome this problem, a novel algorithm named as gbest-guided gravitational search algorithm (GG-GSA) has been proposed by utilizing the global best (gbest) solution in the force calculation equation of GSA. The employment of gbest solution in any optimization algorithm is a tough task and can lead to premature convergence. In the proposed algorithm, the gbest solution is used adaptively and is able to achieve a better trade-off between exploration and exploitation. The performance of the proposed algorithm is compared with GSA and its variants on different suites of well-known benchmark test functions. The experimental results show that the GG-GSA performs better than other algorithms for most of the benchmark test functions. Furthermore, to test the ability of the proposed algorithm in solving real world applications, training of feedforward neural network problem is chosen. The results demonstrated the exceptional performance of GG-GSA on real world data-set.

Integrating modified cuckoo algorithm and creditability evaluation for QoS-aware service composition

QoS-aware Web service composition is regarded as one of the fundamental issues in service computing. Given the open and dynamic internet environment, which lacks a central control of individual service providers, we propose in this paper a novel method that seamlessly considers Quality of Service (QoS) and credibility of service providers to achieve optimal service compositions. Instead of using creditability as one of the QoS attributes, we treat it as the overall capability of a service provider to deliver its promised QoS. We aggregate both user experience (i.e., user trust) and track record (i.e., service reputation) of a provider for accurate creditability evaluation. To facilitate user decision making when multiple (and sometimes conflicting) QoS attributes are involved, we develop an automatic weight calculation approach based on rough set theory and a fuzzy analytic hierarchy process, which assigns higher weights to the more discriminative attributes. Finally, to achieve an optimal service composition, a two phase optimization process is employed, where local optimization chooses services based on creditable QoS assessment and global optimization tackles a multi-objective problem using an effective cuckoo search algorithm. Extensive experimental results show that the proposed QoS-aware service composition approach achieves desirable QoS with credibility guarantees. The performance of our proposed approach also significantly outperforms other competitive methods.

Parameter identification for fractional-order chaotic systems using a hybrid stochastic fractal search algorithm

The knowledge about parameters is very important for control and synchronization of fractional-order chaotic systems. In this paper, an effective hybrid stochastic fractal search (HSFS) algorithm is proposed for parameter identification of fractional-order chaotic systems. To enhance the exploration and exploitation abilities, several efficient local search methods are incorporated into the HSFS as hybrid strategies. The performance of the HSFS is comprehensively evaluated on three typical fractional-order chaotic systems. Numerical simulations and comparisons with some existed algorithms demonstrate the effectiveness of the proposed method.

A batching-move iterated local search algorithm for the bin packing problem with generalized precedence constraints

In this paper, we propose a generalisation of the bin packing problem, obtained by adding precedences between items that can assume heterogeneous non-negative integer values. Such generalisation also models the well-known Simple Assembly Line Balancing Problem of type I. To solve the problem, we propose a simple and effective iterated local search algorithm that integrates in an innovative way of constructive procedures and neighbourhood structures to guide the search to local optimal solutions. Moreover, we apply some preprocessing procedures and adapt classical lower bounds from the literature. Extensive computational experiments on benchmark instances suggest that the developed algorithm is able to generate good quality solutions in a reasonable computational time.

Effective local search algorithms for high school timetabling problems

This paper addresses the high school timetabling problem. The problem consists in building weekly timetables for meetings between classes and teachers with the goal of minimizing violations of specific requirements. In the last decades, several mixed-integer programs have been proposed and tested for this family of problems. However, medium and large size instances are still not effectively solved by these programs using state-of-the-art solvers and the scientific community has given special attention to the devising of alternative soft computing algorithms. In this paper, we propose a soft computing approach based on Iterated Local Search and Variable Neighborhood Search metaheuristic frameworks. Our algorithms incorporate new neighborhood structures and local search routines to perform an effective search. We validated the proposed algorithms on variants of the problem using seven public instances and a new dataset with 34 real-world instances including large cases. The results demonstrate that the proposed algorithms outperform the state-of-the-art approaches in both cases, finding the best solutions in 38 out of the 41 tested instances.

Frequency-driven tabu search for the maximum s-plex problem

The maximum s-plex problem is an important model for social network analysis and other studies. In this study, we present an effective frequency-driven multi-neighborhood tabu search algorithm (FD-TS) to solve the problem on very large networks. The proposed FD-TS algorithm relies on two transformation operators (Add and Swap) to locate high-quality solutions, and a frequency-driven perturbation operator (Press) to escape and search beyond the identified local optimum traps. We report computational results for 47 massive real-life (sparse) graphs from the SNAP Collection and the 10th DIMACS Challenge, as well as 52 (dense) graphs from the 2nd DIMACS Challenge (results for 48 more graphs are also provided in the Appendix). We demonstrate the effectiveness of our approach by presenting comparisons with the current best-performing algorithms.

An effective cuckoo search algorithm for large-scale combined heat and power economic dispatch problem

This paper develops an effective cuckoo search algorithm (ECSA) for searching optimal solutions for the problem of combined heat and power economic dispatch. The main task of the problem is to determine the optimal value of power of the pure power generators, of the heat of the pure heat generators and of both power and heat of cogenerators so that fuel cost is minimized while exactly meeting power and heat demands and power and heat limits as well as the complicated feasible operating zone of cogenerators. The proposed ECSA is a newly improved version of conventional cuckoo search algorithm to improve the quality of solutions and reduce the maximum number of iterations based on two modified techniques. The first technique is based on the ratio of the difference between the fitness function value of each solution and the lowest fitness function value of the best current solution to the lowest one to determine an effective operation for producing the second new solution generation. The second technique aims to integrate both previous and current solutions into one group and sort them in the descending order of fitness value. The effectiveness of ECSA has been validated via six cases corresponding to six test systems where the scale of the systems is ranged from the smallest system with four units to the largest one with forty-eight units with valve point loading effects. The comparisons of obtained results with other existing methods have indicated that the proposed ECSA is very effective and robust for finding optimal solutions for the CHPED problem.

Scatter search for mixed blocking flowshop scheduling

Empty or limited storage capacities between machines introduce various types of blocking constraint in the industries with flowshop environment. While large applications demand flowshop scheduling with a mix of different types of blocking, research in this area mainly focuses on using only one kind of blocking in a given problem instance. In this paper, using makespan as a criterion, we study permutation flowshops with zero capacity buffers operating under mixed blocking conditions. We present a very effective scatter search (SS) algorithm for this. At the initialisation phase of SS, we use a modified version of the well-known Nawaz, Enscore and Ham (NEH) heuristic. For the improvement method in SS, we use an Iterated Local Search (ILS) algorithm that adopts a greedy job selection and a powerful NEH-based perturbation procedure. Moreover, in the reference set update phase of SS, with small probabilities, we accept worse solutions so as to increase the search diversity. On standard benchmark problems of varying sizes, our algorithm very significantly outperforms well-known existing algorithms in terms of both the solution quality and the computing time. Moreover, our algorithm has found new upper bounds for 314 out of 360 benchmark problem instances.

Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem

Symbiotic Organisms Search (SOS) algorithm is an effective new metaheuristic search algorithm, which has recently recorded wider application in solving complex optimization problems. SOS mimics the symbiotic relationship strategies adopted by organisms in the ecosystem for survival. This paper, presents a study on the application of SOS with Simulated Annealing (SA) to solve the well-known traveling salesman problems (TSPs). The TSP is known to be NP-hard, which consist of a set of (n−1)!/2 feasible solutions. The intent of the proposed hybrid method is to evaluate the convergence behaviour and scalability of the symbiotic organism's search with simulated annealing to solve both small and large-scale travelling salesman problems. The implementation of the SA based SOS (SOS-SA) algorithm was done in the MATLAB environment. To inspect the performance of the proposed hybrid optimization method, experiments on the solution convergence, average execution time, and percentage deviations of both the best and average solutions to the best known solution were conducted. Similarly, in order to obtain unbiased and comprehensive comparisons, descriptive statistics such as mean, standard deviation, minimum, maximum and range were used to describe each of the algorithms, in the analysis section. The Friedman's Test (with post hoc tests) was further used to compare the significant difference in performance between SOS-SA and the other selected state-of-the-art algorithms. The performances of SOS-SA and SOS are evaluated on different sets of TSP benchmarks obtained from TSPLIB (a library containing samples of TSP instances). The empirical analysis’ results show that the quality of the final results as well as the convergence rate of the new algorithm in some cases produced even more superior solutions than the best known TSP benchmarked results.

Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems

This paper proposes rain-fall optimization algorithm (RFO), a new nature-inspired algorithm based on behavior of raindrops, for solving of real-valued numerical optimization problems. RFO has been developed from a motivation to find a simpler and more effective search algorithm to optimize multi-dimensional numerical test functions. It is effective in searching and finding an optimum solution from a large search domain within an acceptable CPU time. Statistical analysis compared the solution quality with well-known heuristic search methods. In addition, an economic dispatch (ED) optimization problem is run on an IEEE 30-bus test system, and the results, compared with those of recent optimization methods, show RFO performing relatively well, sufficiently effective to solve engineering problems. The constraint-handling strategy of the proposed method for solving ED problem is to generate and work with feasible solutions along all the optimization iterations without any mismatch between electricity demand and the total amount of power generation. Unlike the penalty methods, this strategy is unaffected by parameter setting of applied optimization method and its applicability for solving constrained optimization problems is not hampered. Eventually, its robustness is validated by the results of a sensitivity analysis of the parameters.

Near Real Time Pump Optimization and Pressure Management

Management of existing systems can be interpreted as sets of decisions to make regarding pumps and valves to create hydraulic conditions able to satisfy the demand without operational problems such as pressures lower or higher than the normative pressure values. However, among the large number of combinations, some of them manage to reduce energy consumption, by finding the best operating point for pumps, and also water losses, by finding the best operating point for pressure reducing valves (PRV). Several works may be found in the literature using recent and advanced optimization techniques to define pump and valve operation. However, the processing time to define operational rules is a limiting factor for real time decision-making. Taking into account the need to improve the models in terms of optimal rules to apply in near real-time operations, this work presents a hybrid model (simulator + optimizer) to find pump speeds and PRV set points, aiming at combining energy savings with pressure control while reducing water losses. PSO is applied as the main optimization algorithm, which can also work in cooperation with other bio-inspired concepts to deploy an effective and fast search algorithm. The results allow comparisons with other techniques and show the ability of PSO to find an optimal point of operation

An Effective Harmony Search Algorithm for Solving a No-Wait Hybrid Flow Shop Scheduling Problem with Machine Availability Constraint

This research investigates a no-wait hybrid flow shop scheduling problem. Minimizing the mean tardiness is considered as the objective to develop the optimal scheduling algorithm. Characteristics of our considered problem leads to the complexity of problem. First, no-wait operations. Second, setup time of each job is separated from its processing time and depends upon its preceding job. Third, all of jobs aren’t available at the first of scheduling. In other word, each job has individual ready time. Finally, machines are not continuously available due to the preventive maintenance. An effective harmony search algorithm is used to tackle the mentioned problem. A series of computational experiments is conducted by comparing our algorithm with previous meta-heuristic algorithms like population based simulated annealing (PBSA), Adopted imperialist competitive algorithm (ICA) and hybridization of PBSA and ICA (ICA+PBSA). To achieve reliable results, Taguchi approach is used to define robust parameters’ values for our proposed algorithm. The computational results with random test problems suggest that our proposed harmony search outperforms the three foregoing algorithms.

Searching for Solutions to Fuzzy Logical Equation for Breakdown Diagnosis Using GA

In this study, a GA-based approach is employed to directly find the intervals of solution to the inverse fuzzy inference problem. Moreover an effective search algorithm based on fuzzy reasoning is applied to narrow down search range for the possible breakdown causes. Through the assistance of the developed diagnosis system, an inspector can be more easy and effective to find various possible occurred breakdown causes by judging from the observed symptoms during manufacturing process.

Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms

In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big Bang–Big Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks − five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested.

Directional SUSAN image boundary detection of breast thermogram

Thermography of the breast has been shown to be well suited to detect early signs of breast cancer. This study proposes a novel method for boundary detection of breast thermography images using directional SUSAN method. Among breast thermography image processing steps, breast isolation from background and from each other is an essential stage for proper detection of breast cancer. For this purpose, in this study, breast boundary is grouped into three regions depending on the region property. The algorithm of boundary detection is different for each region. Specially, for bottom breast boundary, directional SUSAN edge detector is presented that uses two rectangle masks to create a directional SUSAN gradient image with emphasis on oblique useful edges and omitting undesirable ones. Then cubic parabolic interpolation is implemented to determine a set of edge points on the boundaries. At last, an effective search algorithm is executed to correct some false points in order to extract breast boundaries accurately. The performance of the proposed approach illustrated by applying on the images of three databases. Experimental results show that this method acts effectively and confirm the accurate boundary detection. Moreover, statistical measures are calculated to indicate the remarkable capabilities of the proposed approach.

A Novel Dummy-Based KNN Query Anonymization Method in Mobile Services

Due to the advances of mobile devices with GPS (Global Positioning System), a user's privacy threat is increased in location based services (LBSs).So, various Location Privacy-Preserving Mechanisms (LPPMs) have been proposed in the literature to address the privacy risks derived from the exposure of user locations through the use of LBSs. However, these methods obfuscate the locations disclosed to the LBS provider using a variety of strategies, most of which come at a cost of resource consumption. Therefore, we propose a privacy-protected KNN query anonymization method based on Bayesian estimation for Location-based services. Unlike previous dummy-based approaches, in our method, the request to the LBS server doesn't contain the genuine user location, so we can't calculate whether meet the threshold condition of two location directly, but must to decision making by transition probability. In addition, our method just requires the server returns the results the client needs. Further, we propose an effective search algorithm to improve the server processing. So it can reduce bandwidth usages and efficiently support K-nearest neighbor queries without revealing the private information of the query issuer. An empirical study shows that our proposal is effective in terms of offering location privacy, and efficient in terms of computation and communication costs. Keyword: Location privacy protection, Location-based services(LBSs),K-nearest neighbor query, Bayesian estimation

A Systematic Design Methodology of Asynchronous SAR ADCs

Successive approximation register (SAR) analog-to-digital converters (ADCs) are widely used in biomedical and portable/wearable electronic systems due to their excellent power efficiency. However, both the design and the optimization of high-performance SAR ADCs are time consuming, even for well-experienced circuit designers. For system designers, it is also hard to quickly evaluate the feasibility of a given specification in a process node. This paper presents a systematic sizing procedure for asynchronous SAR ADCs based on design considerations. A sizing tool based on the proposed design procedure is also implemented, the sizing results of which are highly competitive in comparison with other state-of-the-art manual works. Moreover, the sizing time is relatively short due to the efficient and effective search algorithms employed. In addition to the simulation results, two silicon proofs with different specifications and process nodes are provided to demonstrate the feasibility of this design methodology.

Encrypted image classification based on multilayer extreme learning machine

Nowadays, numerous corporations (such as Google, Baidu, etc.) require an efficient and effective search algorithm to crawl out the images with queried objects from databases. Moreover, privacy protection is a significant issue such that confidential images must be encrypted in corporations. Nevertheless, decrypting and then classifying millions of encrypted images becomes a heavy burden to computation. In this paper, we proposed an encrypted image classification framework based on multi-layer extreme learning machine that is able to directly classify encrypted images without decryption. Experiments were conducted on popular handwritten digits and letters databases. Results demonstrate that the proposed framework is secure, efficient and accurate for classifying encrypted images.

An iterated local search algorithm for water distribution network design optimization

The optimal design of water distribution networks is a complex non‐linear combinatorial optimization problem. It consists in finding the least‐cost pipe configuration that satisfies hydraulic laws and customer requirements, using a limited set of available pipe types. In a previous paper (De Corte and Sörensen, Eur J Oper Res 228 (2013), 1–10), we have argued that state‐of‐the‐art optimization algorithms proposed in this domain are unduly complicated and poorly tested. The main contribution of this article is a straightforward, fast, transparent, and effective iterated local search (ILS) algorithm that has at least equivalent performance when compared to the best approaches in the literature, but has a much simpler algorithmic structure. A full‐factorial experiment is conducted to obtain the heuristic's best parameter settings. Contrary to existing algorithms, the ILS algorithm is additionally shown to perform well on a broad set of much more challenging HydroGen (De Corte and Sörensen, Water Resour Manage 28 (2014), 333–350) test instances. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 67(3), 187–198 2016