Areas Of Search Space Research Articles

A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint

AbstractMedical Solid Wastes (MSWs) are major hazardous materials containing harmful biological or chemical compounds that present public and environmental health risks. The collection and transportation of waste are usually informed by optimized work‐balanced routing based on comprehensive spatial data in urban traffic networks, called a Vehicle Routing Problem (VRP). This may be unsuitable for MSWs as their special category means they impose additional complexity. The present article develops a planar graph‐based cluster‐routing approach for the optimal collection of MSWs informed by a Geospatial Information System (GIS). The problem is first formulated as a mixed integer linear program in road network spatial data, in the context of Tehran city. The work has two key aims: (i) to minimize the total routing cost of MSW collection and transfer to waste landfills; (ii) to balance workload across waste collectors. There are three main contributions of the proposed approach: (i) to simplify the large search space area by converting the road network to a planar graph based on graph theory, spatial parameters, and topological rules; (ii) to use a modified K‐means algorithm for clustering; (iii) to consider average traffic impacts in the clustering stage and momentary traffic in the route planning stage. A planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. An iterative cluster‐first‐route‐second heuristic is employed to solve the proposed routing problem. This heuristic customizes a K‐means algorithm to determine the optimal number and size of clusters (i.e., routes). A Traveling Salesman Problem (TSP) algorithm is applied to regulate the optimal sequence of visits to medical centers. The experimental results show improvements in balancing collectors' workload (i.e., ~4 min reduction in the standard deviation of average travel time) with reductions in travel time (i.e., an average ~1 h for the entire fleet and ~4 min per route). These findings confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes.

Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications

In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving.

Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm.

Path testing is the basic approach of white box testing and the main approach to solve it by discovering the particular input data of the searching space to encompass the paths in the software under test. Due to the increasing software complexity, exhaustive testing is impossible and computationally not feasible. The ultimate challenge is to generate suitable test data that maximize the coverage; many approaches have been developed by researchers to accomplish path coverage. The paper suggested a hybrid method (NSA-GA) based on Negative Selection Algorithm (NSA) and Genetic Algorithm (GA) to generate an optimal test data avoiding replication to cover all possible paths. The proposed method modifies the generation of detectors in the generation phase of NSA using GA, as well as, develops a fitness function based on the paths’ prioritization. Different benchmark programs with different data types have been used. The results show that the hybrid method improved the coverage percentage of the programs’ paths, even for complicated paths and its ability to minimize the generated number of test data and enhance the efficiency even with the increased input range of different data types used. This method improves the effectiveness and efficiency of test data generation and maximizes search space area, increasing percentage of path coverage while preventing redundant data.

Self-adapt reservoir clusterization method to enhance robustness of well placement optimization

One of the most crucial tasks in hydrocarbon field development, it is an optimal wells placement. Simulation-based well placement optimization can be intricate to implement or computationally expensive. In this paper, a novel approach for the reduction of computational challenges by combining population-base optimization algorithm with self-adapt reservoir clusterization method to determine initial well placement position for the similar producing zones is presented. The developed methodology supported initial well placement location and allowed to shrink search space area. As a result, the computational time was decreased by 18% for particle swarm optimization and by 21% for genetic algorithm. Additionally, the project profitability was increased by 0.24% in case of genetic algorithm utilization and 0.59% for particle swarm optimization, what confirms the necessity for precise well placement localization. The average convergence factor for the employed algorithms increased from 18.43% to 60.78% for genetic algorithm and from 41.71% to 46.96% for particle swarm algorithm. An additional advantage of the developed clustering method is a notable reduction in time to reach high solution diversity in particle swarm algorithm. Increasing SOM training time for the large data set is negligible in comparison with full reservoir simulation run. The proposed methodology is based on the typical numerical model without requirement of additional dataset or streamline and can be easily transferred from field to field. The power and the workflow utility are demonstrated by the results of significant improvement for the algorithm's convergence rate and time.

Generalized solution for inverse kinematics problem of a robot using hybrid genetic algorithms

The robot control consists of kinematic control and dynamic control. Control methods of the robot involve forward kinematics and inverse kinematics (IK). In Inverse kinematics the joint angles are found for a given position and orientation of the end effector. Inverse kinematics is a nonlinear problem and has multiple solutions. This computation is required to control the robot arms. A Genetic Algorithm (GA) and Hybrid genetic algorithm (HGA) (Genetic Algorithm in conjunction with Nelder-Mead technique) are proposed for solving the inverse kinematics of a robotic arm. HGA introduces two concepts exploration, exploitation. In an exploration phase, the GA identifies the good areas in entire search space and then exploitation phase is performed inside these areas by using Nelder- mead technique Binary Simulated Crossover and niching strategy for binary tournament selection operator is used. Proposed algorithms can be used on any type of manipulator and the only requirement is the forward kinematic equations, which are easily obtained. As a case study inverse kinematics of a Two Link Elbow Manipulator and PUMA manipulator are solved using GA and HGA in MATLAB. The algorithm is able to find all solutions without any error

Rapid initialization method in real-time deformation monitoring of bridges with triple-frequency BDS and GPS measurements

Rapid initialization with ambiguity fixed resolution plays a key role in real-time Global Navigation Satellite Systems (GNSS) displacement monitoring of bridges. In this study, we propose a rapid initialization method with triple-frequency (TF) BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS) observations. The key step of this method is to form extra-wide-lane (EWL) ambiguity with TF BDS and GPS carrier-phase to reduce the ambiguity search space. In order to improve the speed of ambiguity resolution (AR) further, the strategies of fixing ambiguities in groups sequentially from long wavelength to the short and partial AR (PAR) with elevation and signal-to-noise ratio (SNR) criterions were proposed. Based on the real monitoring data on the Baishazhou Yangtze River Bridge, the AR efficiency was tested. The results illustrate that over 90% EWL ambiguities can be fixed by only single-epoch with TF BDS + GPS. After EWL ambiguity is fixed, the wide-lane (WL) ambiguity success rate could improve from 20% with dual-frequency (DF) BDS + GPS data to 70% with TF BDS + GPS data, 50–80% and 80–90% for the three stations respectively. Since the area of ambiguity search space is reduced by TF observations, the ambiguity search time decrased significantly compared with the DF BDS + GPS. In the Time-to-first-fix (TTFF) test, over 85% of epochs can complete the initialization process by only one epoch with one hour of 10 Hz TF BDS + GPS data. However, DF BDS + GPS has only 25%. As for the initialization time longer than two epochs, the counts of TTFFs of TF BDS + GPS are significantly less than that of DF BDS + GPS. Meanwhile, the integrated BDS and GPS can further enhance the geometry of satellites, which will be better for the precision improvement than the increase of signal frequencies.

Incorporating decision maker\u2019s preferences in a multi-objective approach for the software release planning

BackgroundRelease planning (RP) is one of the most complex and relevant activities in the iterative and incremental software development, because it addresses all decisions associated with the selection and assignment of requirements to releases. There are many approaches in which RP is formalized as an optimization problem. In this context, search-based software engineering (SBSE) deals with the application of search techniques to solve complex problems of software engineering. Since RP is a wicked problem with a large focus on human intuition, the decision maker’s (DM) opinion is a relevant issue to be considered when solving release planning problem. Thus, we emphasize the importance in gathering the DM’s preferences to guide the optimization process through search space area of his/her interests.MethodsTypically, RP is modelled as a multi-objective problem by considering to maximize overall clients satisfaction and minimize project risk. In this paper, we extend this notion and consider DM’s preferences as an additional objective. The DM defines a set of preferences about the requirements allocation which is stored in a preference base responsible for influencing the search process. The approach was validated through an empirical study, which consists of two different experiments, respectively identified as (a) automatic experiment and (b) participant-based experiment. Basically, the former aims to analyze the approach using different search-based algorithms (NSGA-II, MOCell, IBEA, and SPEA-II), over artificial and real-world instances, whereas the latter aims at evaluating the use of the proposal in a real scenario composed of human evaluations.ResultsThe automatic experiment points out that NSGA-II obtained overall superiority in two of the three datasets investigated, positioning itself as a superior search technique for scenarios with few number of requirements and preferences, while IBEA showed to be better for larger ones (with more requirements and preferences). Regarding the participant-based experiment, it was found that two thirds of the participants evaluated the preference-based solution better than the non-preference-based one.ConclusionsThe results suggest that it is feasible to investigate the approach in a real-world scenario. In addition, we made available a prototype tool in order to incorporate the human’s preferences about the requirements allocation into the solution of release planning.

ESCAPE: Effective Scalable Clustering Approach for Parallel Execution of Continuous Position-Based Queries in Position Monitoring Applications

Massive data sets of continuous position-based queries (CPQs) in position monitoring applications offer a challenge of time ingestion while forwarding the position-based data within wireless search space area. As a result of recurrent modifications in network topology due to the mobility of users, processing of large CPQ data sets and roaming CPQs is one of the challenges in position monitoring. Therefore, the parallel algorithm is proposed in this paper for clustering and parallel processing of roaming CPQs which will recognize solid clusters in the wireless search space area. We present an algorithm which proposes the use of search space areas for clustering and introduce a parallel framework for parallel processing of CPQ data sets. The wireless search space area from wireless networks are used for scalable and effective calculation of clusters and dimensions in wireless networks. Present continuous query processing techniques cannot competently process roaming CPQs in the wireless search space area. The proposed algorithm is demonstrated to have practically best speedups in processing roaming CPQs. Results indicate that the proposed work determine improved ability of CPQs over existing mechanisms and attain small query latency and high precision in position monitoring applications.

Parameter Estimation for VSI-Fed PMSM Based on a Dynamic PSO With Learning Strategies

A dynamic particle swarm optimization with learning strategy (DPSO-LS) is proposed for key parameter estimation for permanent magnet synchronous machines (PMSMs), where the voltage-source inverter (VSI) nonlinearities are taken into account in the parameter estimation model and can be estimated simultaneously with other machine parameters. In the DPSO-LS algorithm, a novel movement modification equation with variable exploration vector is designed to effectively update particles, enabling swarms to cover large areas of search space with large probability and thus the global search ability is enhanced. Moreover, a Gaussian-distribution-based dynamic opposition-based learning strategy is developed to help the pBest jump out local optima. The proposed DPSO-LS can significantly enhance the estimator model accuracy and dynamic performance. Finally, the proposed algorithm is applied to multiple parameter estimation including the VSI nonlinearities of a PMSM. The performance of DPSO-LS is compared with several existing PSO algorithms, and the comparison results show that the proposed parameters estimation method has better performance in tracking the variation of machine parameters effectively and estimating the VSI nonlinearities under different operation conditions.

A new hybrid ant colony algorithm for scheduling of no-wait flowshop

In this paper, the no-wait flow shop scheduling problem under makespan and flowtime criteria is addressed. The no-wait flowshop is a variant of the well-known flowshop scheduling problem where all processes follow the previous one without any interruption for operations of a job. Owing to the problem is known to be NP-hard for more than two machines, a hybrid meta-heuristic algorithm based on ant colony optimization (ACO) and simulated annealing (SA) algorithm is improved. First, at each step, due to the characteristic of ACO algorithm that include solution construction and pheromone trail updating, some different areas of search space are checked and best solution is selected. Then, to enhance the quality and diversity of the solution and finding best neighbor of this solution, a novel SA is presented. Moreover, a new principle is applied for global pheromone update based on the probability function like SA algorithm. The proposed approach solution is compared with several the state-of-the-art algorithms in the literature. The reported results show that the proposed algorithms are effective and the new approach for local search in ACO algorithm is efficient for solving the no-wait flow shop problem. Then, we employed another hybrid ACO algorithm based on hybridization of ACO with variable neighborhood search (VNS) and compare the results given by two proposed algorithms. These results show that our new hybrid provides better results than ACO-VNS algorithm.

Commercial Aircraft Lateral Flight Reference Trajectory Optimization

Cruise is normally the longest and most expensive phase in a long-haul flight. Taking advantage of winds to reduce flight time using a constant mach number is of real interest to the aeronautical industry. Reducing flight time will reduce time related costs, help passengers to catch their connections, and reduce fuel consumption. Saving fuel also leads to fewer polluting emissions released to the atmosphere. The “free-flight” concept is adopted here, with the search space area modeled as a graph in which the weather information at each vertex is obtained from predictions from Environment Canada. The Floyd-Warshall optimization algorithm was implemented to allow the flight reference trajectory optimization to have automatic decision making capacities to deliver the shortest path trajectory between departure and destination airports. Simulations showed that flight time savings as high as 19 minutes could be obtained, representing (roughly estimated) a savings of 1.2 tons of fuel.

Opposition-based learning in the shuffled bidirectional differential evolution algorithm

The opposition-based learning (OBL) strategy by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. In this paper, the OBL is employed to speed up the shuffled bidirectional differential evolution (SBDE) algorithm. The SBDE by employing the partitioning, shuffling and bidirectional optimization concepts increases the number and diversity of search moves in respect to the original differential evolution (DE). So with incorporating the SBDE and OBL strategy, we can obtain the algorithms with an ability of better exploring the promising areas of search space without occurring stagnation or premature convergence. Experiments on 25 benchmark functions and non-parametric analysis of obtained results demonstrate a better performance of our proposed algorithms than original SBDE algorithm. Also an extensive performance comparison the proposed algorithms with some modern and state-of-the-art DE algorithms reported in the literature confirms a statistically significantly better performance of proposed algorithms in most cases. In a later part of the comparative experiments, firstly proposed algorithms are compared with other evolutionary algorithms (EAs) proposed for special session CEC2005. Then a comparison against a wide variety of recently proposed EAs is performed. The obtained results show that in most cases the proposed algorithms have a statistically significantly better performance in comparable to several existing EAs.

English

Travelling salesman problem (TSP) is a routing problem, which is a sub-problem of many application domains such as transportation, network communication. In this paper, we propose a novel methodology which solves the problem ACO algorithm has defined with well distribution strategy in which total search space area is partition into P numbers of hyper-cubic quadrants where P is the dimension of total search space is updating ant colony optimization heuristic parameter that improve the performance When solving TSP. From experimental Results, we can see that proposed algorithm is giving better showing than other standard algorithms.

LFM Signal Parameters Estimation Using Optimization Approach Initialized by Lipschitz Constant Assisted DIRECT Algorithm

In this paper, the widely used numerical optimization method for estimating linear frequency modulated signal parameters is modified. To this purpose, an improved Dividing RECTangles (DIRECT) algorithm, called the Lipschitz constant assisted DIRECT (L-DIRECT) algorithm, is proposed to substitute for the commonly used Grid Search method. The proposed global optimization algorithm can provide initial estimates for local optimization algorithms such as Newton and Simplex. Based on the classical DIRECT algorithm, the L-DIRECT algorithm eliminates hopeless areas, suspends unlikely areas, and concentrates on promising areas in search space, thus determining the range of attraction for local optimization algorithms with lower SNR thresholds or lessened computational burdens. The effect of the modification is validated by simulation results.

A Cellular-rearranging of Population in Genetic Algorithms to Solve Assembly-Line Balancing Problem

Assembly line balancing problem (ALBP) is the allocating of assembly tasks to workstations with consideration of some criteria such as time and the number of workstations. Due to the complexity of ALB, finding the optimum solutions in terms of the number of workstations in the assembly line needs suitable meta-heuristic techniques. Genetic algorithms have been used to a large extent. Due to converging to the local optimal solutions to the most genetic algorithms, the balanced exploration of the new area of search space and exploitation of good solutions by this kind of algorithms as a good way can be sharpened with some meta-heuristic. In this paper, the modified cellular (grid) rearranging-population structure is developed. The individuals of the population are located on cells according to the hamming distance value among individuals as neighbours before regenerations and a family of cellular genetic algorithms (CGAs) is defined. By using the cellular structure and the rearrangements, some of the family members can find better solutions compared with others in the same iterations, and they behave much more reasonably in order to acquire the solution in terms of the number of workstations and the smoothly balanced task assignment on criteria conditions.

Avoiding and escaping depressions in real-time heuristic search

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is inaccurate compared t...

Pivoted‐pad journal bearings lubrication design

PurposeThis paper aims to utilize an evolutionary algorithm as an optimization technique to solve design optimization of a pivoted‐pad journal bearing performance characteristics considering objective functions which are contradictory to each other. These functions include fluid film thickness, film temperature, and power loss along with design variables vector, which consists of pad axial length to journal diameter ratio, pad arc length, bearing radial clearance, pad offset factor, and pad preload factor.Design/methodology/approachIn order to improve performance characteristics of the pivoted‐pad journal bearing, the genetic algorithm (GA) was employed and the modeling of the bearing was represented with the finite element method. Also, a numerical method, global criterion, was conducted to validate the efficiency and applicability of the proposed method.FindingsThis paper utilizes the GA to solve multi‐objective optimization of the pivoted‐pad journal bearing performance governed by design variables such as pad axial length to journal diameter ratio, pad arc length, bearing radial clearance, pad offset factor, and pad preload factor for a range of rotational speed. The global criterion method (GCM) proves that the proposed method is very efficient and useful for the optimal design of the pivoted‐pad journal bearings. An acceptable results agreement between the GA and the GCM has been obtained. The GA is well suited to find the optimum solutions with a high probability since it starts with an initial set of solutions rather than a single solution. The GA explores different areas of search space in parallel and, it is good at exploitation.Originality/valueWith the help of the proposed method, it will be possible to have an idea about bearing geometric parameters on a more rational basis, and to better predict the dynamic performance of rotor systems supported on the pivoted‐pad journal bearings.

The whittled design space

The paper by Woodbury and Burrow is examined using four criteria: completeness, discrimination, alternative approaches, and combining exploration in different problem domains. Although the paper covers significant aspects of the search space literature it leaves out some relevant aspects of the cognitive approach. It fares much better in terms of discriminating important concepts and alternative approaches to the modeling of the design search space. Thestructure–function–behaviormodel is suggested as an analogy for the central parameters of the search space paradigm. The Woodbury and Burrow paper reveals more than what has been accomplished up to now in the design search space area, but its task still remains incomplete.

Parallel standard cell placement algorithms with quality equivalent to simulated annealing

An algorithm called heuristic spanning creates parallelism by simultaneously investigating different areas of the plausible combinatorial search space. It is used to replace the high-temperature portion of simulated annealing. The low-temperature portion of simulated annealing is sped up by a technique called section annealing, in which placement is geographically divided and the pieces are assigned to separate processors. Each processor generates simulated-annealing-style moves for the cells in its area and communicates the moves to other processors as necessary. Heuristic spanning and section annealing are shown experimentally to converge to the same final cost function as regular simulated annealing. These approaches achieve significant speedup over uniprocessor simulated annealing, giving high-quality VLSI placement of standard cells in a short period of time. >