Local Search Process Research Articles

A combination of FA and SRPSO algorithm for Combined Heat and Power Economic Dispatch

This paper represents a hybrid Firefly and Self-Regulating Particle Swarm Optimization (FSRPSO) algorithm to solve optimal Combined Heat and Power Economic Dispatch (CHPED) problem. Valve point effect on fuel cost function of pure generation units, electrical power losses in transmission systems and feasible operating zones are taken into account in the CHPED problem. The CHPED refers to minimize total costs of fuel for electricity and heat generation supply to load demand. The proposed FSRPSO attempts to determine the start of the local search process properly by checking the previous global best. Thus, the FSRPSO is able to exploit strong points of both Firefly Algorithm (FA) and SRPSO mechanisms in order to balance between exploration and exploitation phases. Besides, for the sake of validation the proposed hybrid method, the FSRPSO is examined on 21 well-known benchmarks, and also a real engineering case i.e., two power systems for evaluating its performance compared with the SRPSO, FA, PSO, and other state-of-the-art algorithms. The obtained optimization results show that the proposed FSRPSO provides fast, mature and reliable optimum solutions and outperform other compared algorithms in diverse categories of benchmarks along with the studied CHPED problem.

Raccoon optimization algorithm-based accurate positioning scheme for reliable emergency data dissemination under NLOS situations in VANETs

In emergency situations, cooperative positioning of vehicular nodes is essential for facilitating precise and stable information for achieving reliable data dissemination in Vehicular Ad hoc NETworks (VANETs). However, the existence of Non-Line-Of-Sight (NLOS) nodes degrades the accuracy in estimating ranging measurements introduced by the blockages from tall vehicles and buildings. In this paper, Raccoon Optimization Algorithm-based Accurate Positioning Scheme (ROA-APS) is proposed for improving the accuracy in the estimation of ranging measurements in order to determine the exact position of NLOS nodes. It is proposed for ensuring maximized reliability and reduced latency in the event of warning message exchange. It inherits the food rummaging style of real raccoons for speeding and strengthening the local and global search process involved in the estimation of NLOS node positions. It utilizes maximum probability of acquiring higher adaptability through active learning to attain better localization of NLOS nodes. It inherits the distance information for calculating the position accuracy associated with vehicle trajectory, distance information error and the number of vehicles. It also uses the method of weighted average to enforce more confidence to the distance information provided by neighboring nodes. The simulation experiments of the proposed ROA-APS using EstiNet simulators are conducted to determine its significance with respect to positioning accuracy, emergency message dissemination rate, positioning error, neighbor vehicles awareness rate and positioning time. The results confirm an increased mean emergency message dissemination rate, positioning accuracy and neighbor vehicles awareness rate by 16.21%, 14.38% and 15.16% when compared to the benchmarked schemes.

An automated breast cancer diagnosis using feature selection and parameter optimization in ANN

Detecting and treating breast cancer at earlier stages is highly proved to improve the survival rate of breast cancer patients as breast cancer is considered a major cause of death worldwide. Classical methods for diagnosing breast cancer depend on human expertise and they incur huge amounts of labor, time and are subject to human error. An Integrated Artificial Immune system and Artificial Bee Colony based breast cancer diagnosis (IAIS-ABC-CDS) is proposed for parallel processing of effective feature selection and parameter optimization in an Artificial Neural Network (ANN). The IAIS-ABC-CDS with Momentum-based Gradient Descent Backpropagation (MBGD) that uses the advantages of Simulated Annealing (SA) for enhancing local search process is compared to the benchmark diagnosis schemes of IAIS-ABC-CDS with Resilient Back-Propagation Techniques (RBPT) and Genetic Algorithm based ANN with Multilayer Perceptron (GA-ANN-MLP) schemes. The proposed IAIS-ABC-CDS is confirmed to produce a mean classification of 99.34% and 99.11% in ANN under the Wisconsin dataset.

A study of hybridization of abc for continuous function optimization- a survey

Swarm intelligence algorithms are meta heuristics that simulates the nature for solving optimization problems, Artificial Bee Colony (ABC) algorithm is one of the most recent nature inspired algorithms which used for problem optimization, numerous research efforts has been concentrated in this particular area. However, the Artificial Bee Colony performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The Artificial Bee Colony is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions, in order to overcome these limitations as well as to broaden the scope and viability of nature inspired algorithms many variations of this algorithm are being presented and the results being very amazing. This paper presents an overview of some of the hybridized meta heuristics with Artificial Bee Colony algorithm for continuous function optimization; many benchmark functions have been used to show the validity of every approach.

Tribe-charged system search for global optimization

In this paper, an improved metaheuristic algorithm called Tribe-Charged System Search is proposed for global optimization. In this improved algorithm, the main searching loop of the standard Charged System Search algorithm is modified to achieve a better convergence performance. In this algorithm, the searching phase of the algorithm is divided into three distinct phases called "Tribes" in which the searching process is conducted differently in each phase based on the communication allowance between tribes. These changes cause the algorithm focuses on high global searching for the early iterations while the concentrating in local search process is considered through the last iterations. In order to validate the performance of the new algorithm, 4 mathematical benchmark functions alongside the 3 well-known constrained problems and 2 engineering design problems are utilized. The performance of the new algorithm is compared to six other metaheuristic algorithms. The results of the mathematical, constrained and engineering problems prove that the new method is capable of providing very competitive results among the other metaheuristics.

An improved iterated greedy algorithm for the distributed assembly permutation flowshop scheduling problem

This paper considers a distributed assembly permutation flowshop scheduling problem (DAPFSP) with total flowtime (TF) criterion, which is of great significance to both industry and research community. We propose an improved iterative greedy algorithm based on the groupthink (gIGA) for solving the problem. Firstly, based on the solution representation, we present an effective initialization procedure by combining the well-known NEH heuristic and Palmer method. Secondly, in order to improve the efficiency of the algorithm, both the destruction reconstruction process and the local search process are elaborately designed for the products and jobs separately. Next, our algorithm adaptively extracts jobs in the destruction stage with regard to the size of instances. In addition, we employ a novel selection method that is based on the objective values and the ages of the individuals in the population. Through a total of 810 benchmark instances, the proposed algorithm is compared with seven state-of-the-art algorithms in the literature. The experimental results show that the proposed algorithm performs significantly better than the other algorithms in comparison by three analytical methods for solving the DAPFSP with TF criterion.

Neighborhood centroid opposite-based learning Harris Hawks optimization for training neural networks

The Harris Hawks Optimization Algorithm is a new metaheuristic optimization that simulates the process of Harris Hawk hunting prey (rabbit) in nature. The global and local search processes of the algorithm are performed by simulating several stages of cooperative behavior during hunting. To enhance the performance of this algorithm, in this paper we propose a neighborhood centroid opposite-based learning Harris Hawks optimization algorithm (NCOHHO). The mechanism of applying the neighborhood centroid under the premise of using opposite-based learning technology to improve the performance of the algorithm, the neighborhood centroid is used as a reference point for the generation of the opposite particle, while maintaining the diversity of the population and make full use of the swarm search experience to expand the search range of the reverse solution. Enhancing the probability of finding the optimal solution and the improved algorithm is superior to the original Harris Hawks Optimization algorithm in all aspects. We apply NCOHHO to the training of feed-forward neural network (FNN). To confirm that using NCOHHO to train FNN is more effective, five classification datasets are applied to benchmark the performance of the proposed method. Comprehensive comparison and analysis from the three aspects of mean, variance and classification success rate, the experimental results show that the proposed NCOHHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other metaheuristic algorithms in terms of the performance measures.

An adaptive switchover hybrid particle swarm optimization algorithm with local search strategy for constrained optimization problems

Practical engineering optimization problems are almost constrained optimization problems and difficult to be solved effectively, therefore, how to handle these problems has attracted more and more attention. Particle swarm optimization (PSO) is one of the most popular algorithms in solving the complicated optimization problems due to its relatively strong global optimization capability and low requirement for computing resources. However, PSO is easy to converge prematurely like other swarm intelligence algorithms due to the loss of diversity among particles. This article proposes an adaptive switchover hybrid PSO framework with local search process (ASHPSO), which adaptively switches the optimization searching process between the standard PSO and the differential evolution (DE) modified by a full dimension crossover strategy to avoid the premature convergence problem. Moreover, a local search strategy is employed to improve the boundary search capability of PSO in consideration of the engineering problems characteristics. Experiments on 28 well-known benchmark functions, 5 engineering problems and a full vehicle multi-disciplinary optimization problem demonstrate the effectiveness of the proposed algorithm compared with other hybrid variants.

An improved memetic algorithm for the flexible job shop scheduling problem with transportation times

In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divided into general solutions and local optimal solutions according to the elite library. The general solutions are executed by the simulated annealing algorithm to improve the quality, and the local optimal solutions are executed by the mutation operation to increase the diversity of the solution set. Comparison experiments with the improved genetic algorithm show that the improved memetic algorithm has better search performance and stability.

Improvement of solution using local search method by perturbation on VRPTW variants

One application of graph theory is to optimize the distribution problem. This problem can be solved using Vehicle Routing Problem with Time Window (VRPTW) model and its variants such as VRPTW, CVRPTW dan OVRPTW. This article comprehends the improvement of the solution with the local search method using perturbation on those variants. There are three parts in the method: generating an initial solution, improvement using local search, and perturbation. The initial solution was generated using the sequential insertion algorithm, the local search process used inter-route and intra-route operators, and the perturbation using ejection chain and double swap. Result of experiments showed that perturbation using double swap gave a better solution than ejection chain. This caused by two-times movement in the double swap that could examine all optimal solution possibilities. An example of implementation the VRPTW variant on distribution optimization is given in this article.

Hybrid Bees Approach based on Improved Search Sites Selection by Firefly Algorithm for Solving Complex Continuous Functions

The Bees Algorithm (BA) is a recent population-based optimization algorithm, which tries to imitate the natural behavior of honey bees in food foraging. This meta-heuristic is widely used in various engineering fields. However, it suffers from certain limitations. This paper focuses on improvements to the BA in order to improve its overall performance. The proposed enhancements were applied alone or in pair to develop enhanced versions of the BA. Three improved variants of BA were presented: BAMS-AN, HBAFA and HFBA. The new BAMS-AN includes memory scheme in order to avoid revisiting previously visited sites and an adaptive neighborhood search procedure to escape from local optima during the local search process. HBAFA introduces the Firefly Algorithm (FA) in local search of BA to update the positions of recruited bees, thus increasing exploitation in each selected site. The third improved BA, i.e. HFBA, employs FA to initialize the population of bees in the BA for a best exploration and to start the search from more promising regions of the search space. The proposed enhancements to the BA have been tested using several continuous benchmark functions and the results have been compared to those achieved by the standard BA and other optimization techniques. The experimental results indicate that the improved variants of BA outperform the standard BA and other algorithms on most of the benchmark functions. The enhanced BAMS-AN performs particularly better than others improved BAs in terms of solution quality and convergence speed.

Contention-Aware Mapping and Scheduling Optimization for NoC-Based MPSoCs

Network-on-Chip (NoC) has emerged as an alternative interconnecting paradigm in the state-of-the-art multi-core architectures. Its capability of parallel data transfer over various paths increases the possibility of resource contention and causes network congestion. How to avoid contention, as well as to optimize performance and energy consumption becomes a great challenge for system designers. In this paper, we consider spatial and temporal aspects of communication to avoid contention. In spatial aspect, we propose to utilize overlapped communication paths to estimate the degree of contention, such that they can be minimized in mapping stage. In temporal aspect, we sequentialize data transfer with potential contention by introducing additional latency. To optimize the design concerns with the corresponding constraint model, we further provide an efficient algorithm to search for better solutions for the problem, which integrates a local search process into a genetic algorithm and applies various heuristics in initialization and evolution process. Experimentations from random and real-case benchmarks demonstrate the efficiency of our method in multi-objective optimization and the effectiveness of our techniques in avoiding network contention, while keeping performance and energy consumption optimized.

Global search in single-solution-based metaheuristics

PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

Chaotic memetic algorithm and its application for detecting community structure in complex networks.

Community structure is one of the most important topological characteristics of complex networks. Detecting the community structure is a highly challenging problem in analyzing complex networks and it has high significance for understanding the function and organization of complex networks. A wide range of algorithms for this problem uses the maximization of a quality function called modularity. In this paper, a Chaotic Memetic Algorithm is proposed and used to solve the problem of the community structure detection in complex networks. In the proposed algorithm, the combination of the genetic algorithm (global search) and a dedicated local search is used to search the solution space. In addition, to improve the convergence speed and efficiency, in both global search and local search processes, instead of random numbers, chaotic numbers are used. By using chaotic numbers, the population diversity is preserved and it prevents from falling in the local optimum. The experiments on both real-world and synthetic benchmark networks indicate that the proposed algorithm is effective compared with state-of-the-art algorithms.

OEbBOA: A Novel Improved Binary Butterfly Optimization Approaches With Various Strategies for Feature Selection

Binary butterfly optimization approach (bBOA) is a recent high performing feature selection algorithm presented in 2018 which is based on the food foraging behavior of butterflies. This paper tries to improve the structure of the bBOA to enhance its classification accuracy, dimension reduction and reliability in feature selection task for who are interested in the fields of data mining and pattern recognition. The new initialization strategy and differential evolution strategy are applied to reduce the randomness of bBOA's initialization and local search process. Then, a new parameter is added to make the bBOA's transfer function more adaptive to the change of exploration and exploitation. Besides, evolution population dynamics (EPD) mechanism is employed as an extension of bBOA. The new method called optimization and extension of binary butterfly optimization approaches (OEbBOA) is tested with the K nearest neighbor classier in which twenty UCI datasets and seven recent algorithms are utilized to assess the performance of the OEbBOA algorithm. The experimental results and nonparametric Wilcoxons rank sum test confirm the efficiency of the proposed OEbBOA in maximizing classification accuracy while minimizing the number of features selected.

A model for tourists' information search behaviour on Ghanaian cuisine


 
 
 The aim of this study is to explore the information-seeking behaviour of international tourists in relation to Ghanaian cuisine. The study employed both the origin and destination context to gain an understanding of international tourists’ local food information search process. A grounded theory approach was used for both data collection and analysis. A total of 40 in-depth interviews were conducted in the process. The paper shows that most international tourists do not search or engage in an adequate information search on local food before their journeys. The findings further reveal that contextual conditions such as tourist typology and repeat visit shape tourists’ level of knowledge on local food, which in turn affect the information search strategies adopted at the destination. It is concluded that international tourists visiting Ghana are generally ignorant of the local cuisine; therefore, the Ghana Tourism Authority should find innovative ways of getting information on local cuisine to tourists upon arrival into the country.
 
 

Robust visual tracking based on global-and-local search with confidence reliability estimation

Visual object tracking is an open and challenging problem, an online tracker must be able to keep track of the target object for a long time period even in complex scenarios, such as target drift and background occlusion. Discriminative correlation filters (DCF) have shown excellent performance in short-term target tracking problems thanks to their circular dense sampling mechanism and fast computation with a discrete Fourier transform. However, they tend to drift from the target when the target encounters drastic deformation, fast motion, or background occlusion. This can result in a bad model update since the tracker searches the target in a local region centered at the position where target was located in the previous frame. There is no recovery mechanism for target re-identification and re-location. To handle this issue, this paper proposes a global-and-local-search technique that applies a DCF-based tracking model with a novel target-aware detector in a collaborative way. Our tracking model performs the local search process with high tracking confidence, and the target-aware detector is executed to re-identify and locate the target via global search from the entire frame when the model instability and confidence fluctuation are detected by proposed tracking system. Additionally, we designed an enhanced peak-to-sidelobe ratio (EPSR) for confidence estimation, which indicates system instability and fluctuation degree. Thus, the local tracking model and target-aware detector are collaboratively applied for both final target state estimation and online model updates. This not only avoids model corruption from bad updates, but also prevents our tracker from drifting problems for long-term tracking. Experiments on OTB-100 and VOT2016 benchmarks demonstrate that the proposed tracking method achieves state-of-the-art tracking performance in terms of accuracy and robustness, with 22 fps tracking speed (close to realtime) run on a single GPU.

Local search in speciation-based bloat control for genetic programming

This work presents a unique genetic programming (GP) approach that integrates a numerical local search method and a bloat-control mechanism to address some of the main issues with traditional GP. The former provides a directed search operator to work in conjunction with standard syntax operators that perform more exploration in design space, while the latter controls code growth by maintaining program diversity through speciation. The system can produce highly parsimonious solutions, thus reducing the cost of performing the local optimization process. The proposal is extensively evaluated using real-world problems from diverse domains, and the behavior of the search is analyzed from several different perspectives, including how species evolve, the effect of the local search process and the interpretability of the results. Results show that the proposed approach compares favorably with a standard approach, and that the hybrid algorithm can be used as a viable alternative for solving real-world symbolic regression problems.

How humans initiate energy optimization and converge on their optimal gaits.

A central principle in motor control is that the coordination strategies learned by our nervous system are often optimal. Here, we combined human experiments with computational reinforcement learning models to study how the nervous system navigates possible movements to arrive at an optimal coordination. Our experiments used robotic exoskeletons to reshape the relationship between how participants walk and how much energy they consume. We found that while some participants used their relatively high natural gait variability to explore the new energetic landscape and spontaneously initiate energy optimization, most participants preferred to exploit their originally preferred, but now suboptimal, gait. We could nevertheless reliably initiate optimization in these exploiters by providing them with the experience of lower cost gaits, suggesting that the nervous system benefits from cues about the relevant dimensions along which to re-optimize its coordination. Once optimization was initiated, we found that the nervous system employed a local search process to converge on the new optimum gait over tens of seconds. Once optimization was completed, the nervous system learned to predict this new optimal gait and rapidly returned to it within a few steps if perturbed away. We then used our data to develop reinforcement learning models that can predict experimental behaviours, and applied these models to inductively reason about how the nervous system optimizes coordination. We conclude that the nervous system optimizes for energy using a prediction of the optimal gait, and then refines this prediction with the cost of each new walking step.

Enhanced Metaheuristic Optimization: Wind-Driven Flower Pollination Algorithm

The flower pollination algorithm is a new metaheuristic optimization technique that simulates the pollination behavior of flowers in nature. The global and local search processes of the algorithm are performed by simulating the self-pollination and cross-pollination of flowers. However, the conventional flower pollination algorithm has several limitations. To overcome the problem of slow convergence and prevent the algorithm from becoming stuck around local optimum, this paper describes an enhanced metaheuristic wind-driven flower pollination algorithm (WDFPA). Experiments are conducted using 29 benchmark test functions and two engineering design problems, and the proposed WDFPA is compared against other metaheuristic optimization algorithms and several classical optimization approaches. The results show that WDFPA achieves better performance than the conventional flower pollination algorithm, especially in high-dimensional optimization problems. The convergence speed and accuracy of WDFPA exhibit significant improvements over other metaheuristic algorithms in many of the test cases. Additionally, WDFPA produces optimal results for engineering design problems involving a welded beam and a spring structure.