Solutions In Search Space Research Articles

Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

This paper addresses the parameter estimation problem for a manufacturing process based on the Auto-Regressive Moving Average (ARMA) model. The accurate estimation of the ARMA model’s parameter helps to reduce the production costs, provide better product quality, increase productivity and profit. Meta-heuristic algorithms are among these approximate techniques which have been successfully used to search for an optimal solution in complex search space. Meta-heuristic algorithms can converge to an optimal global solution despite traditional parameter estimation techniques which stuck by local optimal. A comparison between Meta-heuristic algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Accelerated PSO, Cuckoo Search, Krill Herd and Firefly algorithm is provided to handle the parameter estimation problem for a Winding process in the industry. The developed ARMA-meta-heuristics models for a winding machine are evaluated based on different evaluation metrics. The results reveal that meta-heuristics can provide an outstanding modeling performance.

A Game Theoretic Approach for the Real-Life Multiple-Criterion Vehicle Routing Problem With Multiple Time Windows

This paper presents a new framework for the optimization of real-life multiple-objective vehicle routing problems with multiple time windows. This framework uses a hybrid variable neighborhood tabu search heuristic that chooses Pareto nondominated solutions from the search space of solutions that satisfy a set of Nash equilibrium conditions for a multiple-agent game theory model. Even though the framework is general and can tackle different classes of vehicle routing problems, it is herein tested on three objectives: minimizing the total travel cost (expressed in time units), maximizing the minimal customers’ utility, and maximizing the minimal drivers’ utility. The results on real-life instances provided by a Canadian transportation company highlight the benefits of the multiple-criteria model; an important motivation to the transportation industry for its real-life implementation.

Evolutionary algorithms for generating urban morphology: Variations and multiple objectives

Morphological variation of urban tissues, which evolve through the optimisation of multiple conflicting objectives, benefit significantly from the application of robust metaheuristic search processes that utilise search and optimisation mechanisms for design problems that have no clear single optimal solution, as well as a solution search space that is too large for a ‘brute-force’ manual approach. As such, and within the context of the experiments presented within this article, the rapidly changing environmental, climatic and demographic global conditions necessitates the utilisation of stochastic search processes for generating design solutions that optimise for multiple conflicting objectives by means of controlled and directed morphological variation within the urban fabric.

Numerical Optimization Using the Heart Algorithm

In this article the authors investigate the application of the heart algorithm for solving unconstraint numerical optimization problems. Heart algorithms are a novel optimization algorithm which mimics the heart function and circulatory system procedure in the human beings. It starts with a number of candidate solutions for the given problem and utilizes the contraction and expansion actions to move the candidates in the search space for finding optimal solution. The applicability and performance of the heart algorithm for solving unconstrained optimization problems has been tested using several benchmark functions. Experimental results show its potential and superiority.

Mathematical Optimization Algorithm for Minimizing the Cost Function of GHG Emission in AS/RS Using Positive Selection Based Clonal Selection Principle

This paper regards with the minimization of total cost of Greenhouse Gas (GHG) efficiency in Automated Storage and Retrieval System (AS/RS). A mathematical model is constructed based on tax cost, penalty cost and discount cost of GHG emission of AS/RS. A two stage algorithm namely positive selection based clonal selection principle (PSBCSP) is used to find the optimal solution of the constructed model. In the first stage positive selection principle is used to reduce the search space of the optimal solution by fixing a threshold value. In the later stage clonal selection principle is used to generate best solutions. The obtained results are compared with other existing algorithms in the literature, which shows that the proposed algorithm yields a better result compared to others.

Fitness varying gravitational constant in GSA

Gravitational Search Algorithm (GSA) is a recent metaheuristic algorithm inspired by Newton’s law of gravity and law of motion. In this search process, position change is based on the calculation of step size which depends upon a constant namely, Gravitational Constant (G). G is an exponentially decreasing function throughout the search process. Further, in-spite of having different masses, the value of G remains same for each agent, which may cause inappropriate step size of agents for the next move, and thus leads the swarm toward stagnation or sometimes skipping the true optima. To overcome stagnation, we first propose a gravitational constant having different scaling characteristics for different phase of the search process. Secondly, a dynamic behavior is introduced in this proposed gravitational constant which varies according to the fitness of the agents. Due to this behavior, the gravitational constant will be different for every agent based on its fitness and thus will help in controlling the acceleration and step sizes of the agents which further improve exploration and exploitation of the solution search space. The proposed strategy is tested over 23 well-known classical benchmark functions and 11 shifted and biased benchmark functions. Various statistical analyzes and a comparative study with original GSA, Chaos-based GSA (CGSA), Bio-geography Based Optimization (BBO) and DBBO has been carried out.

Opportunistic mining of top-n high utility patterns

Mining high utility patterns is an important data mining problem that is formulated as finding patterns whose utilities are no less than a threshold. As the mining results are very sensitive to such a threshold, it is difficult for users to specify an appropriate one. An alternative formulation of the problem is to find the top-n high utility patterns. However, the second formulation is more challenging because the corresponding threshold is unknown in advance and the solution search space becomes even larger. When there are very long patterns prior algorithms simply cannot work to mine top-n high utility patterns even for very small n.This paper proposes a novel algorithm for mining top-n high utility patterns that are long. The proposed algorithm adopts an opportunistic pattern growth approach and proposes five opportunistic strategies for scalably maintaining shortlisted patterns, for efficiently computing utilities, and for estimating tight upper bounds to prune search space. Extensive experiments show that the proposed algorithm is 1 to 3 orders of magnitude more efficient than the state-of-the-art top-n high utility pattern mining algorithms, and it is even up to 2 orders of magnitude faster than high utility pattern mining algorithms that are tuned with an optimal threshold.

Convolutional Neural Network Hyper-Parameters Optimization based on Genetic Algorithms

In machine learning for computer vision based applications, Convolutional Neural Network (CNN) is the most widely used technique for image classification. Despite these deep neural networks efficiency, choosing their optimal architecture for a given task remains an open problem. In fact, CNNs performance depends on many hyper-parameters namely CNN depth, convolutional layer number, filters number and their respective sizes. Many CNN structures have been manually designed by researchers and then evaluated to verify their efficiency. In this paper, our contribution is to propose an innovative approach, labeled Enhanced Elite CNN Model Propagation (Enhanced E-CNN-MP), to automatically learn the optimal structure of a CNN. To traverse the large search space of candidate solutions our approach is based on Genetic Algorithms (GA). These meta-heuristic algorithms are well-known for non-deterministic problem resolution. Simulations demonstrate the ability of the designed approach to compute optimal CNN hyper-parameters in a given classification task. Classification accuracy of the designed CNN based on Enhanced E-CNN-MP method, exceed that of public CNN even with the use of the Transfer Learning technique. Our contribution advances the current state by offering to scientists, regardless of their field of research, the ability of designing optimal CNNs for any particular classification problem.

Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing

Emerging transportation network services, such as customized buses, hold the promise of expanding overall traveler accessibility in congested metropolitan areas. A number of internet-based customized bus services have been planned and deployed for major origin-destination (OD) pairs to/from inner cities with limited physical road infrastructure. In this research, we aim to develop a joint optimization model for addressing a number of practical challenges for providing flexible public transportation services. First, how to maintain minimum loading rate requirements and increase the number of customers per bus for the bus operators to reach long-term profitability. Second, how to optimize detailed bus routing and timetabling plans to satisfy a wide range of specific user constraints, such as passengers’ pickup and delivery locations with preferred time windows, through flexible decision for matching passengers to bus routes. From a space-time network modeling perspective, this paper develops a multi-commodity network flow-based optimization model to formulate a customized bus service network design problem so as to optimize the utilization of the vehicle capacity while satisfying individual demand requests defined through space-time windows. We further develop a solution algorithm based on the Lagrangian decomposition for the primal problem and a space-time prism based method to reduce the solution search space. Case studies using both the illustrative and real-world large-scale transportation networks are conducted to demonstrate the effectiveness of the proposed algorithm and its sensitivity under different practical operating conditions.

Determining Approximate Solutions of Nonlinear Ordinary Differential Equations Using Orthogonal Colliding Bodies Optimization

The solution of nonlinear Ordinary Differential Equations (ODE) finds potential applications in physics, economics, computing and engineering. Conventional approaches used for solving ODE are effective in case of 1st order or 2nd order problems. With increase in order the complexity associated with the problem increases. Thus instead of going for an exact solution, determining an approximate solution is also helpful. In this paper, solving ODE is handled as an optimization problem. Popular Fourier Series expansion is used as an approximation function. A hybrid algorithm Orthogonal Colliding Bodies Optimization (OCBO) is formulated by assembling good features of orthogonal array (exploration of solution in search space) and Colliding Bodies Optimization (bodies after collision quickly move to a position with minimal energy level on the search space). The coefficients of the Fourier series are computed with OCBO. Simulation studies are carried out to determine solution of popular practically used ODEs: Bernoulli Equation for flowing fluids, Integro-Differential equation, Brachistochrone equation for gravity, current response of an oscillatory Tank circuit, voltage and current decay with time in an electrical circuit. Simulations are also carried out on three benchmark ODEs used for modelling the biological processes. Comparative analysis demonstrated the superior approximation of the proposed approach over Orthogonal PSO, water cycle algorithm and Harmonic search.

A Hybrid Approach for Power System Security Enhancement via Optimal Installation of Flexible AC Transmission System (FACTS) Devices

Increasing demand for electricity has placed heavy stress on power system security. Therefore, this paper focuses on the problem of how to maximize power system static security in terms of branch loading and voltage level under normal operation and even the most critical single line contingency conditions. This paper proposes a hybrid approach to find out the optimal locations and settings of two classical types of flexible AC transmission system (FACTS) devices, namely thyristor-controlled series compensators (TCSCs) and static var compensators (SVCs) for solving this problem. Our proposed approach requires a two-step strategy. Firstly, the min cut algorithm (MCA) and tangent vector technique (TVT) are applied to determine the proper candidate locations of TCSC and SVC respectively so as to reduce the search scope for a solution to the problem, and then the cuckoo search algorithm (CSA) is employed to solve this problem by simultaneously optimizing the locations and settings for TCSC and SVC installation. The proposed hybrid approach has been verified on the IEEE 6-bus and modified IEEE 14-bus test systems. The results indicate that CSA outperforms particle swarm optimization (PSO), proving its effectiveness and potential, and they also show that our proposed hybrid approach can find the best locations and settings for TCSC and SVC devices as an effective way for enhancing power system static security by removing or alleviating the overloads and voltage violations under normal operation and even the most critical single line contingency conditions. Using this hybrid approach, the search space for solution to the problem becomes limited hence the computational burden will be decreased.

An improved Opposition-Based Sine Cosine Algorithm for global optimization

A new method to solve global optimization and engineering problems called OBSCA.The proposed method improves the SCA by using opposite-based learning.We apply the OBSCA over mathematical benchmark functions.We test OBSCA in engineering optimization problems.Comparisons support the improvement on the performance of OBCSA. Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces.

Improved Bernstein Optimization Based Nonlinear Model Predictive Control Scheme for Power Systems

This paper presents a improved Bernstein global optimization algorithm based model predictive control (MPC) scheme for the nonlinear systems. A new improvement in the Bernstein algorithm is the introduction of a box pruning operator, which during a branch-and-bound search, discard portions of the solution search space that do not contain global solution, thereby speeding up the algorithm. The applicability of this MPC scheme is demonstrated with a simulation studies on a nonlinear single machine infinite bus power system over a wide range of operating conditions. The simulation results show improvement in the system damping and settling time compared with the classical power system stabilizer and partial feedback linearization control schemes.

Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.

Bio-inspired heuristics hybrid with sequential quadratic programming and interior-point methods for reliable treatment of economic load dispatch problem

In the present study, bio-inspired computational heuristics are exploited for finding the solution of economic load dispatch (ELD) problem with valve point loading effect using variants of genetic algorithm (GA) hybrid with sequential quadratic programming (SQP) and interior-point algorithms (IPAs). Variants of GAs are constructed using different sets of routines for its fundamental operators in order to explore the entire search space for global optimum solutions while SQP and IPA are integrated with GAs for rapid local convergence. Nine variants of each design scheme based on GAs, GA-SQP and GA-IPAs are applied on three different ELD problems of thermal power plant systems. Comparative studies of the proposed schemes are performed through the results of statistical performance indices in order to establish the worth and effectiveness in terms of accuracy, convergence and complexity measures.

Algorithms for the joint multitasking scheduling and common due date assignment problem

In this paper, we investigate a joint multitasking scheduling and common due date assignment problem on a single machine, for which examples can be found in product delivery process in logistics. Multitasking allows the machine to perform multiple tasks. The multitasking phenomenon has been observed in various practical domains, including manufacturing and administration. In multitasking settings, each waiting job interrupts a currently in-processing job, causing an interruption time and a switching time. In common due date assignment problems, the objective is to determine the optimal value of this due date with the purpose of minimising a total penalty function, which is associated with service quality. For the problem with general interruption functions, analytical properties are obtained to reduce the search space of the optimal solutions. For the cases with linear interruption functions, we develop a polynomial-time algorithm. Numerical experiments have been conducted to validate the efficiency of our proposed algorithm. Computational results also demonstrate an interesting phenomenon that in some cases, the optimal solutions under multitasking are superior to the counterparts without multitasking. Besides, we also devise a mixed integer programme for the cases with linear interruption function.

Bi-criteria single-machine batch scheduling with machine on/off switching under time-of-use tariffs

The industrial sector is the largest consumer of the world’s total energy and most of its consumption is in form of electricity. In recent years, to strengthen the peak load regulation capability, time-of-use (TOU) pricing has been implemented in many countries to encourage consumers to shift their use from peak to mid- and off-peak periods such that their energy bills can be reduced. In this paper, we study a new single-machine batch scheduling problem with machine on/off switching under TOU tariffs, which aims to simultaneously minimize total electricity cost and makespan. For the problem, we first develop a bi-objective mixed-integer linear programming model. Based on optimal batch rule analysis, an improved model is further provided which greatly reduces Pareto optimal solution search space. To efficiently solve large-size problems, we propose a heuristic based ε-constraint method. The results from extensive computational experiments confirm the effectiveness and efficiency of the proposed model and the algorithm.

Design of reduced search space strategy based on integration of Nelder–Mead method and pattern search algorithm with application to economic load dispatch problem

This paper presents a novel hybrid technique for the solution of economic load dispatch problem with valve point loading effect using Nelder–Mead (NM) simplex method and pattern search (PS) algorithm. Strength of globalized NM optimization algorithm has been employed to explore the search space for near optimal solution, and PS algorithm is used in combination with a search space reduction strategy, incorporating the principles of selection and stochastic reproduction, to fine-tune the result. The proposed technique has been applied to three different systems having 3, 13 and 40 generating units to demonstrate the application for small to large load dispatch set-up. The efficacy of the design scheme is established from comparison of the results with the state-of-the-art solvers, and it is found that the proposed scheme gives the best result in terms of mean cost while the average computational time is less than most of the reported methods.

Transmission expansion planning systems using algorithm genetic with multi-objective criterion

In this paper the problem of planning the expansion of the transmission network (PERT) by means of a genetic algorithm on principles of multi-objective optimization (AGM) resolved. The proposed model uses an appropriate coding to optimize the PERT. In addition, appropriate genetic operators developed for efficient search space of feasible solutions. The model solves simultaneously: Minimize investment costs PERT, improve voltage levels at nodes and minimize losses on the lines of the transmission system. The AGM is tested in the system Garver 6 nodes and 24 nodes system IEEE_RTS achieving the best solutions.

Block-type high temperature gas cooled reactor reloading pattern optimization using genetic algorithm

The reactor reloading pattern optimization is a typical combinatorial optimization problem with a huge search space. It is very hard for traditional optimization algorithm to find the global optimal solution in such huge search space. However, for combinatorial optimization problem, the genetic algorithm (GA) provides a very effective solution by its excellent adaptive ability and optimization ability. This paper is focused on the reloading pattern optimization by using GA in a block-type high temperature gas cooled reactor(HTGR) and corresponding programs were written to realize this goal. To improve the calculation accuracy of core physics, the transport calculation with 26 groups is adopted in the core calculation, which will also be time-consuming. To make up for this shortcoming, the parallel optimization of GA is carried out. Finally, a refueling optimization benchmark in a small HTGR is constructed to test the optimization ability of GA. The results show that GA has a good optimization ability and computational stability for reloading pattern optimization in block-type HTGRs.