Solution Search Process Research Articles

Archimedean spiral based artificial bee colony algorithm

Swarm intelligence techniques are popular meta-heuristics which are developed to solve very complex problems that are not decipherable using traditional optimization approaches. In this class, artificial bee colony is latest and efficient algorithm that is able to solve complex problems. During solution search process it perform exploration in the early iterations, while should utilize the identified area in later stage. By analyzing the ABC, it is clear that the position update process depends on the difference of two random vector multiplied by a random component. So the step size of ABC is exceedingly hinge on these arbitrary components that leads to skipping of good solutions. For that reason, in this paper, Archimedean Spiral inspired Local Search (ASLS) algorithm is united with ABC and the new algorithm is labelled as Archimedean Spiral based ABC (ASABC) algorithm. In ASABC, in the current swarm only good solutions get chance to exploit the nearby search space using ASLS local search approach in every iteration. To demonstrate the performance of ASABC, experiment performed over 10 recognized benchmarks and compared with basic ABC, Best-so-far ABC and Modified ABC. Outcomes prove that ASABC algorithm may be considered as a viable algorithm for optimization problems.

Effective competency based differential evolution algorithm

The DE algorithm is considered as an efficient algorithm in the field of evolutionary algorithms. In case of DE the solution search process is a combination of evolutionary (evolution of population) and swarm intelligence (position update of individual) search process. In DE, the diversification and convergence of the population is controlled by the crossover rate (CR), the scale factor (F) and the vector differences of three randomly selected individuals. In DE algorithm, every solution is given equal chance to take part in the solution search and in case of stagnation; it is difficult to get out from this situation. Therefore, a competency based position update process is integrated in DE to boost the speed of convergence in addition to the diversification ability of the algorithm. The accuracy, efficiency, robustness, and reliability of the proposed algorithm, namely efficient competency based DE (ECDE) are analyzed over a set of 20 benchmark problems with diverse characteristics. Results are compared with DE and its two recent variants, namely FBDE, FSADE, and other algorithms, namely ABC and PSO.

Hierarchical measurement strategy for cost-effective interpolation of spatiotemporal data in manufacturing

High-resolution spatiotemporal data is crucial for characterizing, modeling, and monitoring the space–time dynamics of complex systems in manufacturing. However, the acquisition of such data is generally expensive and time-consuming. Spatiotemporal interpolation aims to predict the values at unmeasured locations using measured data, and emerges as a promising solution to cost-effectively characterizing spatiotemporal processes. Since the interpolation performance is largely influenced by the available measurement data, an intelligent measurement strategy is an important prerequisite to the success of interpolation methods. In this paper, a hierarchical measurement strategy is developed to achieve a balance between interpolation precision and measurement cost in spatiotemporal interpolation. A hierarchical decision-making problem is formulated to determine the observation times and measurement locations at each observation. To expedite the solution search process, hierarchical genetic algorithm is adopted and implemented using high-performance computing. Moreover, a new form of the covariance function is developed using a Bessel additive periodic variogram to more accurately model the periodic spatial variations in spatiotemporal processes. Case studies using real-world data collected from ultrasonic metal welding are reported to demonstrate the effectiveness of the proposed method.

ОПТИМІЗАЦІЯ ПРУЖНО-ДЕМПФЕРНИХ ПІДВІСОК ОБЛАДНАННЯ СИСТЕМИ КОНДИЦІОНУВАННЯ ПОВІТРЯ ЛІТАКА

The design of equipment overload protection in an aircraft air-conditioning unit requires the use of sufficiently effective methods for analyzing the vibration characteristics of the suspension, as well as the development of an appropriate synthesis method for the specific task. To solve the problem applied the adaptive hybrid optimization method. The object of optimization is the design of the suspension of the air conditioning unit with an asymmetrical distribution of masses and arrangement of joints. Joints are the attachment points in the form of shock absorbers and bellows for joining with pipes. Block is considered solid. Links will be highlighted as optimized elements. The aim of the work is to improve the method of optimizing the parameters of shock absorbers, which provide protection from vibration and operating shock loads. The proposed optimization method uses a number of methods that are called hybridients. Sets the criterion by which the most effective hybridients are selected. This criterion includes information that characterizes a changing situation; namely, signs of the structure and metric parameters of the space where the search is carried out; the prehistory of the computational process by which the possible continuation is established; the nature of the system of functions that determine the problem being solved. Adaptive control is introduced, which carries out the getting of decision vectors, search directions and search steps, respectively, the changing situation. Since it is possible to effectively minimize mass only with joint minimization of overload which is the parameter, to the boundary of which the solution search process goes, it is natural to use multi-criteria optimization. In this case, it is at the same time optimizing mass and overload. Restrictions are imposed on the structural dimensions and strength of the shock absorbers. As a result of the optimization, the weight of the suspension elements and the overload acting on the suspended block are reduced. The limits to which mass and overload can be reduced are determined.

Inhibitory control and memory in the search process for a modified problem in grey squirrels, Sciurus carolinensis

Inhibiting learned behaviours when they become unproductive and searching for an alternative solution to solve a familiar but different problem are two indicators of flexibility in problem solving. A wide range of animals show these tendencies spontaneously, but what kind of search process is at play behind their problem-solving success? Here, we investigated how Eastern grey squirrels, Sciurus carolinensis, solved a modified mechanical problem that required them to abandon their preferred and learned solution and search for alternative solutions to retrieve out-of-reach food rewards. Squirrels could solve the problem by engaging in either an exhaustive search (i.e., using trial-and-error to access the reward) or a ‘backup’ solution search (i.e., recalling a previously successful but non-preferred solution). We found that all squirrels successfully solved the modified problem on their first trial and showed solving durations comparable to their last experience of using their preferred solution. Their success and high efficiency could be explained by their high level of inhibitory control as the squirrels did not persistently emit the learned and preferred, but now ineffective, pushing behaviour. Although the squirrels had minimal experience in using the alternative (non-preferred) successful solution, they used it directly or after one or two failed attempts to achieve success. Thus, the squirrels were using the ‘backup’ solution search process. Such a process is likely a form of generalisation which involves retrieving related information of an experienced problem and applying previous successful experience during problem solving. Overall, our results provide information regarding the search process underlying the flexibility observable in problem-solving success.

ОПТИМІЗАЦІЯ ТЕХНІЧНОГО ОБСЛУГОВУВАННЯ СКЛАДНИХ ОБ'ЄКТІВ РАДІОЕ-ЛЕКТРОННОЇ ТЕХНІКИ

The article discusses models for optimizing the maintenance eservice process (MS) of complex objects of radio-electronic technics (RET). The statement of the problem determining optimal parameters of objects MS is formulated for the case if the state maintenance strategy MS (MSS strategy) is used. As a criterion of optimization, the requirement minimum unit cost of operating an object for a given period of its operation is used, provided that the required level of failure-free operation of the object is estimated as estimated by “mean time on failures”. The objective functions of the optimization task are determined by modeling the process of MS and repair (MSandR) of the object. This circumstance explains the choice of a search method for an approximately optimal solution to the problem: simplest relaxation method of direct enumeration is used, controlled by a human expert who solves this problem in the interactive dialogue mode between the user and the computer. The methodology for determining approximately optimal parameters of strategy MSS described in this article is intended for use in the development of object RET. The methodology allows the early stages of development to pre-evaluate possibility of increasing level reliability of the facility due MS. At later stages of development, when all technical solutions have already become known, elements for which there are measurable determining parameters become known, preliminary estimates of the necessary parameters MSS can be refined and corrected design decisions. Corresponding refinement of calculations should be made every time when accurate data on the reliability of component parts appear. Software (SW) was developed for computer support of the solution search process. SW developed by Delphi programming tools.

A Methodology to Perform Air Traffic Complexity Analysis Based on Spatio-Temporal Regions Constructed Around Aircraft Conflicts

One of the main missions of air traffic management is to guarantee en route safety. This safety is quantified through some minimum separation distance between pairs of flying aircraft. Current systems are human-based, i.e., have human air traffic controllers assuring minimum separation is maintained and in cases a loss of separation is predicted, they take actions to prevent the occurrence of such events. The constant and rapid increment of the air traffic demand is pushing current air traffic control systems to their limits. Development of automatic decision support systems, which can be used to automate, or support aircraft conflict detection and resolution, is considered a possible solution. However, the combinatorial nature of the problem poses several challenges for such a task. Metrics or various analytical procedures to produce information about the complexity of given scenarios can be used to guide the solution search process. In this paper, we present a complexity analysis based on the spatio-temporal interdependencies identified by the use of spatio-temporal regions constructed around the aircraft conflicts.

Integrated Optimization of Differential Evolution with Grasshopper Optimization Algorithm

This paper proposes a scheme to improve the differential evolution (DE) algorithm performance with integrated the grasshopper optimization algorithm (GOA). The grasshopper optimization algorithm mimics the behavior of grasshopper. The characteristic of grasshoppers is slow movement in the larval stage but sudden movement in the adulthood which seem as exploration and exploitation. The grasshopper optimization algorithm concept is added to DE to guide the search process for potential solutions. The efficiency of the DE/GOA is validated by testing on unimodal and multimodal benchmarks optimization problems. The results prove that the DE/GOA algorithm is competitive compared to the other meta-heuristic algorithms.

Autonomous Treatment Plan Optimization Strategy Augmented By Using a Knowledge-Guided and Irregularly Downsampled Voxelization Scheme

Current IMRT/VMAT planning is tedious and labor intensive due to its involvement of manual trial-and-error adjustment of model parameters. Despite of intensive research devoted to autonomous treatment planning, a truly efficient and reliable approach in a clinical environment has yet to be developed. A major shortcoming of the existing approaches is their reliance of DVH domain for characterization of a treatment, which is degenerate and does not always provide sufficient guidance to the solution search process. the use of voxel domain, on the other hand, is hampered by the huge dimensionality and thus intractable computational efficiency. The purpose of the work is to mitigate the problem by implementing an autonomous planning strategy in a novel dose-anatomy guided downsampling voxel scheme and showcase the novel approach by planning several clinical head and neck and pancreatic VMAT treatments in a commercial planning system. A sparse voxelization scheme, referred to as the isodose-feature preserving voxelization (IFPV), introduced recently by our group for concise representation of a treatment plan, is used to represent prior knowledge and to facilitate the optimization. Briefly, anatomical and dosimetric information of a treatment plan is characterized by IFPV, which partitions the voxels into subgroups according to their geometric, anatomical and dosimetric features. For a given patient, the IFPV clusters are generated based on prior treatment reference plans with similar anatomy. A plan is then generated using a commercial treatment planning system (TPS) with its planning parameters initialized with the features of the clusters. API script programming is employed to interact with the TPS to implement autonomous plan evaluation and automated optimization parameter update. The procedure proceeds in an iterative fashion until reaching an acceptable tradeoff among target dose coverage/homogeneity and sparing of critical structures. In each iteration, the IFPV clusters are updated based on current dose distributions. Clinical cases with different sites are tested to demonstrate the proposed approach. Compared with the organ DVH-based approach, it is found that the optimization with IFPV voxelization is much easier to be guided toward the desired solution. This especially favors our autonomous planning approach and makes autonomous optimization more efficient. In addition, because of more effective use of both geometric and dose information during plan optimization, the IFPV voxelization-based inverse planning typically yields better treatment plans, as indicated by the testing clinical cases. This work presents the first implementation of IFPV voxelization into a commercial TPS platform for autonomous treatment planning. The algorithm developed here is directly translatable to widespread clinical used, thus the approach has the potential to significantly improve the radiation therapy workflow and patient care.

A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking

Abstract Due to its simplicity and efficiency, a recently proposed optimization algorithm, Sine Cosine Algorithm (SCA), has gained the interest of researchers from various fields for solving optimization problems. However, it is prone to premature convergence at local minima as it lacks internal memory. To overcome this drawback, a novel Hybrid SCA-PSO algorithm for solving optimization problems and object tracking is proposed. The P b e s t and G b e s t components of PSO (Particle Swarm Optimization) is added to traditional SCA to guide the search process for potential candidate solutions and PSO is then initialized with P b e s t of SCA to exploit the search space further. The proposed algorithm combines the exploitation capability of PSO and exploration capability of SCA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 23 classical, CEC 2005 and CEC 2014 benchmark functions. Statistical parameters are employed to observe the efficiency of the Hybrid SCA-PSO qualitatively and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The Hybrid SCA-PSO algorithm is applied for object tracking as a real thought-provoking case study. Experimental results show that the Hybrid SCA-PSO-based tracker can robustly track an arbitrary target in various challenging conditions. To reveal the capability of the proposed algorithm, comparative studies of tracking accuracy and speed of the Hybrid SCA-PSO based tracking framework and other trackers, viz., Particle filter, Mean-shift, Particle swarm optimization, Bat algorithm, Sine Cosine Algorithm (SCA) and Hybrid Gravitational Search Algorithm (HGSA) is presented.

Neural network and fuzzy system for the tuning of Gravitational Search Algorithm parameters

A good trade-off between exploration and exploitation to find optimal values in search algorithms is very hard to achieve. On the other hand, the combination of search methods may cause computational complexity increase problems. The Gravitational Search Algorithm (GSA) is a swarm optimization algorithm based on the law of gravity, where the solution search process depends on the velocity of particles. The application of intelligent techniques can improve the search performances of GSA. This paper proposes the design of a Neuro and Fuzzy Gravitational Search Algorithm (NFGSA) to achieve better results than GSA in terms of global optimum search capability and convergence speed, without increasing the computational complexity. Both the algorithms have the same computational complexity O(nd), where n is the number of agents and d is the search space dimension. The main task of the designed intelligent system is to adjust a GSA parameter on a revised version of GSA. NFGSA is compared with GSA, a Plane Surface Gravitational Search Algorithm (PSGSA) and a Modified Gravitational Search Algorithm (MGSA). The results show that NFGSA improves the optimization performances of GSA and PSGSA, without adding computational costs. Moreover, the proposed algorithm is better than MGSA for a benchmark function and achieves similar results for two test functions. The analysis on the computational complexity shows that NFGSA has a better computational complexity than MGSA, because NFGSA has complexity O(nd), whereas MGSA has complexity O((nd)2).

On distributed user-centric memetic algorithms

A user-centric memetic algorithm (UcMA) represents an instance of the so-called interactive evolutionary computation, in which the subjacent algorithm that interacts with a human consists of a memetic algorithm (MA) that manages knowledge of the problem with the aim of accelerating the solution search process. UcMAs have been proved to be effective optimization methods to tackle problems that require human intervention (in the form of evaluations/decisions). This paper proposes two generic schemas to distribute a number of (interactive/proactive) UcMAs that act as independent agents and, eventually, synchronize to interchange information. These schemas can be instantiated via a number of parameters (including the cooperation topology) to develop novel 2-dimensional spatially structured cooperative UcMAs. These algorithms can be viewed as specific instances of the so-called parallel MAs but with the particularity that they are especially suited to dealing with combinatorial problems whose solving requires subjective evaluations. An experimental study over microarray ordering problems is done including distinct instances of an NP-hard problem with strong implications in biomedicine and molecular biology, namely the gene ordering problem. It is shown that some distributed UcMAs, especially those based on proactivity, which have been instantiated from our proposals, efficiently handle these problems.

Symmetry-Eliminating Design Space Exploration for Hybrid Application Mapping on Many-Core Architectures

Large scale many-core systems are able to execute concurrently changing mixes of different parallel applications. Hybrid application mapping combines the strengths of design-time exploration/analysis of resource constellations for task-to-core mappings with the flexibility of choosing concrete mappings at run time. However, state-of-the-art design space exploration (DSE) techniques so far ignore the problem of symmetries in modern heterogeneous architectures: not only recurring patterns in the architecture but the mapping of tasks to instances of the same processor type may unnecessarily increase the search space by redundant, symmetrical implementations which typically affects the quality of the DSE. As a remedy, we propose a novel meta-heuristic DSE approach that eliminates architectural symmetries by abstracting the problem to a clustering of tasks and their mapping to processor types. However, we demonstrate that simple task clustering and type mappings may again introduce encoding symmetries in our search space. Thus, we present a formulation of the task clustering and type mapping as a 0–1 integer linear program (ILP) which eliminates all architectural as well as encoding symmetries from the search space. We also contribute a formal feasibility check to ensure that only implementations with at least one feasible concrete mapping are considered. To further improve the search process for feasible solutions, we apply satisfiability modulo theories-like learning techniques: from each infeasible implementation, we extract conditions why the implementation is infeasible and enrich our 0–1 ILP by additional constraints continuously during the DSE. Experimental results show that a DSE equipped with the novel symmetry-eliminating search space and the proposed learning techniques clearly outperforms a state-of-the-art approach known from literature in terms of the quality of the gained implementation classes.

Coordinating assignment and routing decisions in transit vehicle schedules: A variable-splitting Lagrangian decomposition approach for solution symmetry breaking

This paper focuses on how to coordinate a critical set of assignment and routing decisions in a class of multiple-depot transit vehicle scheduling problems. The assignment decision aims to assign a set of transit vehicles from their current locations to trip tasks in a given timetable, where the routing decision needs to route different vehicles to perform the assigned tasks and return to the depot or designated layover locations. When applying the general purpose solvers and task-oriented Lagrangian relaxation framework for real world instances, a thorny issue is that different but indistinguishable vehicles from the same depot or similar locations could commit to the same set of tasks. This inherent solution symmetry property causes extremely difficult computational barriers for effectively eliminating identical solutions, and the lower bound solutions could contain many infeasible vehicle-to-task matches, leading to large optimality gaps. To systematically coordinate the assignment and routing decisions and further dynamically break symmetry during the solution search process, we adopt a variable-splitting approach to introduce task-specific and vehicle-distinguishable Lagrangian multipliers and then propose a sequential assignment process in order to enhance the solution quality for the augmented models with tight formulations. We conduct the numerical experiments to offer the managerial interpretation and examine solution quality of the proposed approach in a wider range of applications.

Self balanced particle swarm optimization

In the field of swarm intelligence inspired algorithms, particle swarm optimization (PSO) is a renowned meta-heuristic due to its simplicity, performance, and implementation. However, the PSO also have some downsides like stagnation and slow convergence due to improper balance between the diversification and convergence abilities of the population. Therefore, in this paper, solution search process of PSO algorithm is modified to balance the organization of the individuals in the search space. In the proposed approach, artificial bee colony (ABC) algorithm inspired fitness-based solution search process is incorporated with the PSO algorithm. The proposed approach is tested over 20 unbiased benchmark functions, and the reported results are compared with PSO 2011, ABC, differential evaluation, self-adaptive acceleration factor in PSO, and Mean PSO algorithms through proper statistical analyses.

Memory based Hybrid Dragonfly Algorithm for numerical optimization problems

A novel hybrid algorithm (MHDA) based on Dragon Fly and PSO is proposed.Performance is tested using standard benchmark problems.Proposed algorithm is compared with well-known optimization algorithms.Statistical analysis is done using Friedmans test and Wilcoxon signed ranksum test.Superiority of MHDA is also proved by applying on engineering design problems. Dragonfly algorithm (DA) is a recently proposed optimization algorithm based on the static and dynamic swarming behaviour of dragonflies. Due to its simplicity and efficiency, DA has received interest of researchers from different fields. However, it lacks internal memory which may lead to its premature convergence to local optima. To overcome this drawback, we propose a novel Memory based Hybrid Dragonfly Algorithm (MHDA) for solving numerical optimization problems. The pbest and gbest concept of Particle Swarm optimization (PSO) is added to conventional DA to guide the search process for potential candidate solutions and PSO is then initialized with pbest of DA to further exploit the search space. The proposed method combines the exploration capability of DA and exploitation capability of PSO to achieve global optimal solutions. The efficiency of the MHDA is validated by testing on basic unconstrained benchmark functions and CEC 2014 test functions. A comparative performance analysis between MHDA and other powerful optimization algorithms have been carried out and significance of the results is proved by statistical methods. The results show that MHDA gives better performance than conventional DA and PSO. Moreover, it gives competitive results in terms of convergence, accuracy and search-ability when compared with the state-of-the-art algorithms. The efficacy of MHDA in solving real world problems is also explained with three engineering design problems.

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

ABSTRACTIn multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Multiobjective bilevel optimisation method to solve environmental-economic power generation and dispatch problem with interval data uncertainty

This paper presents a multiobjective bilevel programming (MOBLP) based interval goal programming (IVGP) method for modelling and solving to solve environmental-economic power generation and dispatch (EEPGD) problem using genetic algorithm (GA) in an inexact environment. In the proposed method, the objective functions to be optimised are represented as goals by introducing target intervals for the achievement of objective values in decision horizon. Then, using interval arithmetic rules, the defined planned – interval goals are converted into standard goals as in conventional goal programming (GP) to formulate minsum IVGP model of the problem. In the solution search process, a genetic algorithm (GA) computational scheme is employed in an interactive way to reach the solution of EEPGD problem. The effective use of the approach is illustrated through a case example of the standard IEEE 30-bus 6-generator test system. The model solution is also compared with the solutions obtained by using other approaches studied previously.

A hybrid, auto-adaptive and rule-based multi-agent approach using evolutionary algorithms for improved searching

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.

Fitness based particle swarm optimization

Particle swarm optimization (PSO) is a popular population based approach used to solve nonlinear and complex optimization problems. It is simple to implement and swarm based probabilistic algorithm but, it also has drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to reduce the chance of stagnation, while improving the convergence speed, a new position updating phase is incorporated with PSO, namely fitness based position updating in PSO. The proposed phase is inspired from the onlooker bee phase of Artificial Bee Colony (ABC) algorithm. In the proposed position updating phase, solutions update their positions based on probability which is a function of fitness. This strategy provides more position updating chances to the better solutions in the solution search process. The proposed algorithm is named as fitness based particle swarm optimization (FPSO). To show the efficiency of FPSO, it is compared with standard PSO 2011 and ABC algorithm over 15 well known benchmark problems and three real world engineering optimization problems.