Search Space Of Problem Research Articles

The Effect of Working Memory Updating Ability on Spatial Insight Problem Solving: Evidence From Behavior and Eye Movement Studies.

It still remains uncertain whether working memory updating ability influences spatial insight problem solving and whether working memory updating ability plays a role in the representation restructuring phase. The current study explored the correlation of working memory updating ability and spatial insight problem solving by behavior and eye movement experiments, and the results showed that high working memory updating ability individuals spend significant shorter time to solve spatial insight problem than low working memory updating ability individuals. For participants with high or low working memory updating ability, the underlying mechanism of spatial insight problem solving is sudden rather than incremental, which demonstrated that the working memory updating ability did not influence the representation restructuring phase. Working memory updating ability influences spatial problem solving, and it works critically in the problem space search phase, while the restructuring phase is sudden and immediate, which is not influenced by working memory updating ability. The representation restructuring tends to be spontaneous.

Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

An efficient hybrid CS‐DP application for generation expansion planning problem

This article proposes a new hybrid approach for generation expansion planning (GEP) based on cuckoo search (CS) and dynamic programming (DP). Generally, the GEP problem is known as having very large search space and highly nonlinear complex combinatorial optimization. The global optimal solution is to solve with the full enumeration DP. However, the computation time grows exponentially when the planning horizon and candidate options increase, which is called ‘curse of dimensionality’. To solve this problem, a hybrid CS‐DP approach is proposed with three techniques for solution quality and convergence characteristic enhancement. First, the CS is integrated into the DP structure for the optimum solution of the very large search space problem. Second, the feasible search concept can eliminate infeasible solutions from the searching process. Finally, the memory exploration search is applied to prevent repeated searches. To measure its effectiveness, the proposed method is applied to 15 existing power plants, with 5 candidate options and 2 test cases: 14 year and 20 year study periods. The experimental results are compared with classical and metaheuristic optimizations. The test results indicate that the proposed hybrid CS‐DP achieves higher solution quality and superior convergence characteristics than other methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Sustainable Highway Maintenance: Optimization of Existing Highway Vertical Alignment Considering Pavement Condition

Any change in the geometric configuration of the existing road affects other dependent elements such as travel time, fuel consumption, and existing pavement. This paper quantifies these effects and finds an optimum geometric configuration. Three separate models are developed in this research: (1) the travel time model which predicts vehicles flow speed on the specified geometric condition, traffic composure, and evaluates the imposed costs to the road users; (2) the fuel consumption model, which estimates needed propulsive force and anticipated fuel for vehicles passing through the road; and (3) the pavement rehabilitation cost model considering two main constraints of existing pavement condition and project line elevation. The developed pavement rehabilitation model proposes the best solution for pavement rehabilitation practice and computes associated costs. Particle Swarm Optimization (PSO) is used to find the optimum solution in a minimization problem search space. The proposed model is applied to a real-world case study. Results show that there is an extremely less tendency for change, even with the existence of adverse geometric conditions, when there is a relatively good pavement condition. In the case of deteriorated pavement conditions, economic justification for geometric modification is required.

Merits of using chromosome representations and shadow chromosomes in genetic algorithms for solving scheduling problems

Two conjectures, the use of incomplete chromosome representations and shadow chromosomes may improve the performance of genetic algorithms (GAs), are examined in this study. The examination entails testing distributed flexible job shop scheduling (DFJS) problems subject to preventive maintenance (PM) that involve four scheduling decisions. Genetic algorithms based on a complete chromosome representation that explicitly models the four decisions have been developed previously. By contrast, herein, two incomplete chromosome representations are proposed, whereby the conjectured advantages are two-fold. First, an incomplete chromosome representation models two scheduling decisions, and the remaining two are decoded by heuristic rules designed to ensure the load balance of manufacturing resources. Therefore, scheduling solutions with load imbalance will not be generated, which will help prevent the execution of ineffective searches. Second, a novel method of generating new chromosomes is developed and employed, instead of using traditional genetic operations. These chromosomes, called shadow chromosomes, are generated from good quality scheduling solutions and they may improve performance. Based on these two conjectures, four GAs are proposed. Numerical experiments reveal that each proposed GA outperforms the prior GAs substantially and the two conjectures are thus well justified. These findings shed light on the application of the two conjectures for developing metaheuristic algorithms to solve other high-dimensional space search problems.

Nonconvex utility-based power allocation for cognitive radio MIMO system over fading channels

In this paper, we present a power control algorithm in cognitive radio multiple-input–multiple-output (MIMO) system over time-varying fading channels based on the utility-based framework. In particular, a more general objective function framework is proposed under a long- or short-term transmitting and interference power constraints for secondary users, which results the optimization problem is nonconvex. A stochastic optimization algorithm is proposed based on an improved quantum-behaved particle swarm optimization (IQPSO), which can solve the nonconvex optimization problem efficiently. The acceleration coefficients are adjusted with time-varying equations which can help the particles jump out the local optimum. Due to the characteristics of wave function of quantum Delta potential well model, IQPSO can get better optimal solutions in search space for the stochastic optimization problem. To show the efficiency of the proposed algorithm, the utilities of the MIMO CR system got by IQPSO are compared with other approaches in the literature in different case studies, which show that the proposed algorithm can solve the nonconvex problem efficiently and achieve significant throughput.

Solving non-convex economic load dispatch problem via artificial cooperative search algorithm

According to no free lunch (NFL) theorem, a metaheuristic optimization method is superior to other metaheuristic optimization methods when it has focused on specific class of optimization problems. Thus, this paper focuses on developing artificial cooperative search (ACS) optimization algorithm to solve economic dispatch (ED) problems more precisely with less complexity than other metaheuristic optimization methods. ACS is a recently developed two population search algorithm based on coevolution process with high probability of finding optimal solution in complex and non-convex optimization problems. This merit is provided by balancing exploration of the problem's search space and exploitation of better results through use of two advanced evolutionary operators and only one control parameter. The constraint handling strategy of the proposed method for solving economic power load dispatch problems is to generate and work with feasible solutions along all the optimization iterations without any mismatch between the total amount of electric power generation and electricity demand plus network transmission loss. Unlike the penalty method, this strategy is unaffected by parameter setting of applied optimization method that complicates its applicability for solving economic power load dispatch problems. The feasibility of ACS for solving ED problem is tested on different lossy non-convex test systems of various sizes and complexities. The practical aspects such as satisfaction of power demand constraint, generation limits of generators and value-point loading effect are undertaken to solve ED problem in medium to relatively large-scale electric power systems. Obtained results confirm the ACS's capability in converging to a better solution highly robust within the reasonable computational time in all independent trials; all these as compared with other optimization algorithms reported in the literature for solving lossy non-convex ED problems. The results are analyzed statistically in terms of solution quality and computational efficiency. The statistical analyses reveal that ACS is a potential method to solve economic power load dispatch problems as it provides higher quality solution in comparison with other optimization algorithms.

An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider

AbstractExisting bio-inspired models are challenged with premature convergence among others. In this paper, an adaptive social spider colony optimisation model based on the foraging behaviour of social spider was proposed as an optimisation problem. The algorithm mimics the prey capture behaviour of the social spider in which, the spider senses the presence of the prey through vibrations transmitted along the web thread. Spiders are the search agents while the web is the search space of the optimisation problem. The natural or biological phenomenon of vibration was modeled using wave theory while optimisation theory was considered in optimizing the objective function of the optimisation problem. This objective function was considered to be the frequency of vibration of the spiders and the prey as this is the function that enables the spider differentiates the vibration of the prey from that of neighbouring spiders and therefore forages maximally. To address the parameter tuning problem, the search pattern...

Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory

Feature selection is an important pre-processing step in machine learning and data mining tasks, which improves the performance of the learning models by removing redundant and irrelevant features. Many feature selection algorithms have been widely studied, including greedy and random search approaches, to find a subset of the most important features for fulfilling a particular task (i.e., classification and regression). As a powerful swarm-based meta-heuristic method, particle swarm optimization (PSO) is reported to be suitable for optimization problems with continuous search space. However, the traditional PSO has rarely been applied to feature selection as a discrete space search problem. In this paper, a novel feature selection algorithm based on PSO with learning memory (PSO-LM) is proposed. The goal of the learning memory strategy is designed to inherit much more useful knowledge from those individuals who have higher fitness and offer faster progress, and the genetic operation is used to balance the local exploitation and the global exploration of the algorithm. Moreover, the $k$ -nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle. The proposed method has been evaluated on some international standard data sets, and the results demonstrated its superiority compared with those wrapper-based feature selection methods.

Moth Flame Optimization Based on Golden Section Search and its Application for Link Prediction Problem

Moth Flame Optimization (MFO) is one of the meta-heuristic algorithms that recently proposed. MFO is inspired from the method of moths' navigation in natural world which is called transverse orientation. This paper presents an improvement of MFO algorithm based on Golden Section Search method (GSS), namely GMFO. GSS is a search method aims at locating the best maximum or minimum point in the problem search space by narrowing the interval that containing this point iteratively until a particular accuracy is reached. In this paper, the GMFO algorithm is tested on fifteen benchmark functions. Then, GMFO is applied for link prediction problem on five datasets and compared with other well-regarded meta- heuristic algorithms. Link prediction problem interests in predicting the possibility of appearing a connection between two nodes of a network, while there is no connection between these nodes in the present state of the network. Based on the experimental results, GMFO algorithm significantly improves the original MFO in solving most of benchmark functions and providing more accurate prediction results for link prediction problem for major datasets.

Cooperative parallel grouping genetic algorithm for the one-dimensional bin packing problem

Evolutionary algorithms have been reported to be efficient metaheuristics for the optimization of several NP-Hard combinatorial optimization problems. In addition to their ability to solve difficult and complex problems in reasonable execution times, parallelized versions of evolutionary algorithms are reported to explore and exploit the problem search space more effectively than their sequential counterparts. The Island Model, where the population of a given run is divided into semi isolated subpopulations, is a popular parallelization approach for evolutionary algorithms such as Grouping Genetic Algorithms (GGA). Although the nature of GGAs is very suitable for coarse-grained parallel processing, designing an Island-parallel model for them is not a straightforward task. Selecting the communication topology, deciding migration and assimilation strategies, adjusting the migration rate and frequency, and using efficient diversification techniques are some of the important issues that needs to be covered in a successful Island-parallel Model. In this study, we propose a novel, scalable Island-parallel GGA (IPGGA) for the well-known combinatorial optimization Problem 1D Bin-Packing (1DBPP). We provide a thorough experimental evaluation of the parallel model and report significant improvements on the Hard28 problem instances by outperforming the state-of-the-art genetic algorithms. Additionally, we analyze and evaluate the parallelization parameters of IPGGA with an emphasis on problem search-space diversity and report several interesting results.

Filtering Bayesian optimization approach in weakly specified search space

Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-parameter tuning and more generally for the efficient global optimization of expensive black-box functions. Systems implementing BO have successfully solved difficult problems in automatic design choices and machine learning hyper-parameters tunings. Many recent advances in the methodologies and theories underlying Bayesian optimization have extended the framework to new applications and provided greater insights into the behavior of these algorithms. Still, these established techniques always require a user-defined space to perform optimization. This pre-defined space specifies the ranges of hyper-parameter values. In many situations, however, it can be difficult to prescribe such spaces, as a prior knowledge is often unavailable. Setting these regions arbitrarily can lead to inefficient optimization—if a space is too large, we can miss the optimum with a limited budget, and on the other hand, if a space is too small, it may not contain the optimum point that we want to get. The unknown search space problem is intractable to solve in practice. Therefore, in this paper, we narrow down to consider specifically the setting of “weakly specified” search space for Bayesian optimization. By weakly specified space, we mean that the pre-defined space is placed at a sufficiently good region so that the optimization can expand and reach to the optimum. However, this pre-defined space need not include the global optimum. We tackle this problem by proposing the filtering expansion strategy for Bayesian optimization. Our approach starts from the initial region and gradually expands the search space. We develop an efficient algorithm for this strategy and derive its regret bound. These theoretical results are complemented by an extensive set of experiments on benchmark functions and two real-world applications which demonstrate the benefits of our proposed approach.

Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous

This work addresses the design and validation of a robust monocular vision-based pose initialization architecture for close-range onorbit-servicing and formation-flying applications. The aim is to rapidly determine the pose of a passive space resident object using its known three-dimensional wireframe model and a single low-resolution two-dimensional image collected on board the servicer spacecraft. In contrast to previous works, the proposed architecture is onboard executable and capable of estimating the pose of the client without the use of fiducial markers and without any a priori range measurements or state information. A novel feature detection method based on the filtering of the weak image gradients is used to identify the true edges of the client in the image, even in presence of the Earth in background. The detected features are synthesized using simple geometric constraints to dramatically reduce the search space of the feature correspondence problem, which is solved using the method. This approach is proven to be an order of magnitude faster than the state-of-the-art random sample consensus methods. A fast Newton–Raphson method that minimizes the fit error between corresponding image and model features is employed to refine the pose estimate and to resolve pose ambiguity. The proposed methodology is tested using actual space imagery collected during the PRISMA mission at about a 700 km altitude and a 10 m interspacecraft separation.

Combining a hierarchical task network planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

This work addresses the combination of a symbolic hierarchical task network planner and a constraint satisfaction solver for the vehicle routing problem in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the article describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The vehicle routing problem solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the hierarchical task network planner computes a scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the vehicle routing problem solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the vehicle routing problem. The article presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different configurations of the vehicle routing problem solver and showing the suitability of using this approach.

Optimal detector design for molecular communication systems using an improved swarm intelligence algorithm

The authors optimise the detection process of diffusion-based molecular communication systems utilising the weighted sum detector with appropriate weight values. Interestingly, no optimisation technique has ever been proposed for the calculation of the weights. To this end, they build on the standard particle swarm optimisation (PSO) technique and propose a robust iterative optimisation algorithm, called acceleration-aided PSO (A-APSO). While modified swarm-based optimisation algorithms focus on slight variations of the standard mathematical formulas, in A-APSO, the acceleration variable of the particles in the swarm is also involved in the search space of the optimisation problem. Particularly, they implement the A-APSO algorithm to evaluate the detector's weights that minimise the closed-form expression of the error probability. Their findings reveal that, when employing the A-APSO weights, the error performance is superior to that achieved by utilising the weight values already existing in the literature or those evaluated with the standard PSO algorithm.

A Reference-Based Multiobjective Bacteria Foraging Optimization Technique for QoS Multicast Routing

Nowadays multicast services are gaining high popularity in supporting various and continuous media applications over wired and wireless networks. But the desired quality of service for these applications makes multicast routing optimization a NP Hard problem. In this manuscript, we propose a reference-based multiple objective bacteria foraging algorithm that solves multicasting routing problem, based on user’s preference. Instead of searching for a preferred solution from the obtained Pareto front, we would rather search for a set of solutions in the desired objective space simultaneously. The algorithm starts with a single-step tree construction method to generate multicast trees as initial bacteria population. The Pareto optimal front generated will comprise of solutions, each of which best suits the user’s choice. The multiobjective bacteria foraging optimization algorithm used here is based on the reference-based method, where the user inputs a reference point in the possible region of the problem search space. Hence the Pareto optimal front generated is in the proximity of the given reference point which would help the user in making more feasible and reliable decisions. Simulations have been carried upon various benchmark as well as some random networks to study and analyze the performance of the proposed algorithm with the existing algorithms. The simulation results reveal that our proposed algorithm (RBMOBFO) yields better results in terms of solution quality and convergence speed.

The Roles of Internal Representation and Processing in Problem Solving Involving Insight: A Computational Complexity Perspective

In human problem solving, there is a wide variation between individuals in problem solution time and success rate, regardless of whether or not this problem solving involves insight. In this paper, we apply computational and parameterized analysis to a plausible formalization of extended representation change theory (eRCT), an integration of problem solving by problem space search and insight as problem restructuring which proposes that this variation may be explainable by individuals having different problem representations and search heuristic choices. Our analyses establish not only the intractability of eRCT in general, but also sets of restrictions under which eRCT-based problem solving can and cannot be done quickly. As such, our analyses both prove that several conjectures about what makes problem solving under eRCT possible in practice are incomplete, in the sense that not all factors in the model whose restriction is responsible for efficient solvability are part of the explanation, and provide several new explanations that are complete.

Hybrid simulation-based optimization of discrete parts manufacturing to increase energy efficiency and productivity

This presented research comprises the development of an optimization module for use in a novel production optimization tool – similar in function but not mode of operation to an Advanced Planning System –, with energy efficiency incorporated into its goal system. The optimization features a hybrid-simulation of production systems as an evaluation function. A hybrid simulation has been developed and presented in preceding publications, in order to enable a sufficient consideration of interactions between material flow and the thermal-physical behavior of the production system. The size of the search space for the complex optimization problem necessitates a customized two-phase-optimization method, which is based on a Genetic Algorithm, with the consideration of linear constraints and extended customizations. The results, obtained in a case study featuring a food production facility, show energy savings of around 20 percent together with significant productivity gains.

CS-ABC: a cooperative system based on artificial bee colony to resolve the DNA fragment assembly problem

DNA Fragment Assembly (DFA) problem is one of the most active research areas in bioinformatics. It consists in assembling a set of DNA fragments to determine the complete genome sequence. Because of the large number of fragments to assemble, this problem is classified as a NP-hard optimisation problem. Thus, in order to deal with the large search space of such problem, we propose a new cooperative approach involving a set of metaheuristics. The proposed cooperative approach, named CS-ABC, is based on artificial bee colony algorithm. In this approach, metaheuristics cooperate as bees with artificial bee colony algorithm to improve the exploration and exploitation ability, forming a cooperative system. The use of a set of metaheuristics improves naturally the exploration ability since each one of them explores differently the search space. The communication between these metaheuristics is established through a shared memory. The exploitation is also enhanced by using different efficient DFA methods communicating according to the master-slave model. In the computational experiment we firstly, analyse the proposed method behaviour resolving DFA problem. Then, we compare its performance against numerous DFA methods with noiseless and noisy data based on three models of error. The proposed method has obtained promising and encouraging results.

A Quantum based Evolutionary Algorithm for Stock Index and Bitcoin Price Forecasting

Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms.