Exploitation Of Search Space Research Articles

A Pareto-optimality based black widow spider algorithm for energy efficient flexible job shop scheduling problem considering new job insertion

The utilization of flexible job shops is on the rise, driven by the decreasing life cycle of consumer products. In dynamic flexible job shops, the occurrence of disruptive events like machine breakdown, tool wear, and new job arrivals is common, emphasizing the critical need for efficient scheduling to achieve productivity and cost-effectiveness. Additionally with the growing concerns about energy consumption and global warming, the imperative to contemplate workshops with lower energy usage is becoming increasingly significant. Therefore, in this study a multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with insertion of new jobs for optimization objectives: makespan, total energy consumption and schedule instability is addressed. A novel multi-objective black widow spider algorithm (MOBWSA), inspired by the mating behavior of black widow spiders, is proposed. The MOBWSA ensures an optimal balance between minimizing makespan and energy consumption through its superior evolutionary processes of procreation, mutation and cannibalism. Furthermore, a strategic on/off strategy is implemented to enhance energy efficiency. The disruptions in schedule at rescheduling stage are addressed through the proposed sigmoidal weighted instability function. To handle the multi-objective nature of the problem this work incorporates Pareto-optimality approach in combination with novel hybrid crowding distance metric. The optimization is further aided by exploitation of search space through an ensemble of local search operators. Testing and evaluation of the MOBWSA with 30 benchmark problems is conducted, and its performance for multiple metrics is compared with recently published state-of-the-art algorithms. The results establish the effectiveness and efficiency of the proposed algorithm.

Application of Time Series Analysis and Regression Algorithm in Assessing the Effectiveness of Monetary Policy in the Era of Digital Economy

Monetary policy is the series of actions to manage country's money supply and to attain economic growth. An objection to using interest rates in monetary policy is that the use of interest rates has the problem of time uncertainty. Therefore, this research introduces Conditional Autoregressive Value-at-Risk and Henry Gas Solubility Optimization (Caviar-HGSO) with Deep Long Short Term Memory (DeepLSTM) to monetary policy forecasting to demonstrate its effectiveness. Here, Caviar-HGSO is a hybrid of Caviar and HGSO used to tune DeepLSTM weights. The HGSO algorithm follows the wind state to balance exploration and exploitation in search space while avoiding local optimization. A Caviar model estimates the parameter over time by autoregressive process and determine a parameter by regression analysis. The advantages of these two optimization algorithms lead to better monetary policy forecasts. The experimental results show that Caviar-HGSO_DeepLSTM performs better regarding MAE, MSE, and RMSE, namely 0.438, 0.103, and 0.351.

A single-loop reliability-based design optimization using adaptive differential evolution

Reliability-based design optimization (RBDO) is an efficient tool for generating reliable and optimal solution under uncertainty of design variables. The major challenges in solving RBDO problems are generating optimal or near optimal reliable solution and higher computational cost. In this paper, a single-loop RBDO formulation is developed for addressing these challenges that uses shifting vector approach for achieving feasibility for violated constraints or performance functions. The formulation also incorporates target and trial vectors of differential evolution (DE) for guiding the algorithm. DE is also made adaptive by designing a heuristic parameter that controls two mutation operators for both exploration and exploitation of search space. The proposed RBDO method is tested on three mathematical and four engineering examples. The reliability of obtained solutions from RBDO methods are verified using Monte Carlo simulations with sample size of one million. The results of the proposed method are compared with various RBDO methods from the literature and a double-loop based DE method. It is found that the proposed method generates the best reliable solution for all examples.

Enhanced Parallel-Particle Swarm Optimization (EP-PSO) Approach for Solving Nurse Rostering Problem

A Nurse Rostering Problem is a highly-constrained combinatorial optimization problem, where we assign several nurses to shifts by violating minimum constraints. Due to massive number of constraints, these problems are difficult to handle manually. The advantage of automating the task is to generate a roster having not only high quality but also more flexibility by reducing the workload, time and effort of head nurses. The PSO algorithm is extremely dependent upon settings of control parameters and balance the exploration and exploitation in search space. These problems are avoided by proposed an ES-PSO Algorithm. Highly constrained problems have huge search space to find an optimal solution, hence to cover that space and find the solution in the stipulated time necessary to increase population. However, it may take more time for particle updating and fitness evaluations. To improve execution time of the compute-intensive task, we have used OpenMP and CUDA framework. The adapted algorithm improves the outcome by minimizing penalty and reduces stuff of compute-intensive tasks.

An approach for optimizing multi-objective problems using hybrid genetic algorithms

Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence.

Accelerated grey wolf optimiser for continuous optimisation problems

Grey wolf optimiser (GWO) is a relatively simple and efficient nature-inspired optimisation algorithm which has shown its competitive performance compared to other population-based meta-heuristics. This algorithm drives the solutions towards some of the best solutions obtained so far using a unique mathematical model, which is inspired from leadership behaviour of grey wolves in nature. To combat the issue of premature convergence and local optima stagnation, an enhanced version of GWO is proposed in this paper. The proposed algorithm is named accelerated grey wolf optimiser (A-GWO). In A-GWO, novel modified search equations are developed that enhances the exploratory behaviour of wolves at later generations, and the exploitation of search space is also improved in the whole search process. To validate the performance of the proposed algorithm, set of 23 well-known classical benchmark problems are used. The results and comparison through various metrics show the reliability and efficiency of the A-GWO.

Covariance matrix adaptive strategy for a multi-objective evolutionary algorithm based on reference point

In this article, an effective method, called an adaptive covariance strategy based on reference points (RPCMA-ES) is proposed for multi-objective optimization. In the proposed algorithm, search space is divided into independent sub-regions by calculating the angle between the objective vector and the reference vector. The reference vectors can be used not only to decompose the original multi-objective optimization problem into a number of single-objective subproblems, but also to elucidate user preferences to target a preferred subset of the whole Pareto front (PF). In this respect, any single objective optimizers can be easily used in this algorithm framework. Inspired by the multi-objective estimation of distribution algorithms, covariance matrix adaptation evolution strategy (CMA-ES) is involved in RPCMA-ES. A state-of-the-art optimizer for single-objective continuous functions is the CMA-ES, which has proven to be able to strike a good balance between the exploration and the exploitation of search space. Furthermore, in order to avoid falling into local optimality and make the new mean closer to the optimal solution, chaos operator is added based on CMA-ES. By comparing it with four state-of-the-art multi-objective optimization algorithms, the simulation results show that the proposed algorithm is competitive and effective in terms of convergence and distribution.

An evolutionary approach to schedule deadline constrained bag of tasks in a cloud

Bag of tasks (BoT) is an application model which consists of a large number of independent tasks. In cloud, computing power is offered as virtual machines (VMs) which differ in terms of speed, memory and cost. When such applications are executed on cloud, an optimal allocation of VMs is needed so that the application executes to completion within the deadline and the cost incurred is minimal. Here, the main challenge is to find an optimal trade-off between execution time and execution cost. Genetic algorithms (GA) are evolutionary algorithms which enable to solve multi-objective problems. This paper proposes a novel deadline constrained bi-objective genetic algorithm based scheduler (DBOGA) to schedule a BoT application onto a cloud. A new fitness function is defined. Exploration and exploitation of search space is carried out based on this. An extensive study on the applicability of DBOGA by considering various scenarios is explored.

An evolutionary approach to schedule deadline constrained bag of tasks in a cloud

Bag of tasks (BoT) is an application model which consists of a large number of independent tasks. In cloud, computing power is offered as virtual machines (VMs) which differ in terms of speed, memory and cost. When such applications are executed on cloud, an optimal allocation of VMs is needed so that the application executes to completion within the deadline and the cost incurred is minimal. Here, the main challenge is to find an optimal trade-off between execution time and execution cost. Genetic algorithms (GA) are evolutionary algorithms which enable to solve multi-objective problems. This paper proposes a novel deadline constrained bi-objective genetic algorithm based scheduler (DBOGA) to schedule a BoT application onto a cloud. A new fitness function is defined. Exploration and exploitation of search space is carried out based on this. An extensive study on the applicability of DBOGA by considering various scenarios is explored.

Optimal design of low power high gain and high speed CMOS circuits using fish swarm optimization algorithm

This work presents a swarm intelligent optimization methodology based on swarming principle and collective behaviour of natural species termed as fish swarm optimization algorithm (FSOA). It is applied for the optimization for the design of a Complementary Metal Oxide semiconductor (CMOS) two-stage comparator and a folded cascode operational trans-conductance (FCOTA). The basic idea of FSOA is to model the traditional behavior of fish such as preying, swarming followed by local fish of individual optimization for global search. The suitably chosen control parameters of FSOA balance the exploration and exploitation of search space. Results obtained by SPICE for the two analog circuits, justify the capability of producing a desired result and the superiority of FSOA over other evolutionary optimization algorithms like HS (harmonic search), PSO (particle swarm optimization) and DE (differential evolution) in terms of convergence speed and design performances. The results obtained using FSOA show improved performances for the designed analog circuits. The circuits designed using FSOA take lesser areas for MOS transistors; both dissipate low powers and provide high gains. The performances of FSOA based designed circuits are significantly better than those of reported works. Simulation results obtained from SPICE also prove that FSOA is the best in comparison with the previously reported techniques in terms of the area occupied by CMOS transistors, gain, power etc. Cadence version 5.10.41 is used to perform the simulation using TSMC 0.35μm and TSMC 1.25μm technology parameters for two-stage comparator and FCOTA, respectively.

Biased Multiobjective Optimization and Decomposition Algorithm.

The bias feature is a major factor that makes a multiobjective optimization problem (MOP) difficult for multiobjective evolutionary algorithms (MOEAs). To deal with this problem feature, an algorithm should carefully balance between exploration and exploitation. The decomposition-based MOEA decomposes an MOP into a number of single objective subproblems and solves them in a collaborative manner. Single objective optimizers can be easily used in this algorithm framework. Covariance matrix adaptation evolution strategy (CMA-ES) has proven to be able to strike good balance between the exploration and the exploitation of search space. This paper proposes a scheme to use both differential evolution (DE) and covariance matrix adaptation in the MOEA based on decomposition. In this scheme, single objective optimization problems are clustered into several groups. To reduce the computational overhead, only one subproblem from each group is selected to optimize by CMA-ES while other subproblems are optimized by DE. When an evolution strategy procedure meets some stopping criteria, it will be reinitialized and used for solving another subproblem in the same group. A set of new multiobjective test problems with bias features are constructed in this paper. Extensive experimental studies show that our proposed algorithm is suitable for dealing with problems with biases.

Multi-objective Truss Optimization Using Different Types of the BB-BC Algorithm

Optimum design of truss structures is considered as a benchmark problem in the field of the structural optimization. In order to solve this hard combinatorial problem, it is necessary to implement adequate optimization tool that would provide sufficiently wide range of possible solutions within a reasonable time as well as to obtain good exploration and exploitation of search space. The aim of presented study was to compare efficiency of different multi-objective algorithms in solving this task.

Multi-objective Truss Optimization Using Different Types of the BB-BC Algorithm

Optimum design of truss structures is considered as a benchmark problem in the field of the structural optimization. In order to solve this hard combinatorial problem, it is necessary to implement adequate optimization tool that would provide sufficiently wide range of possible solutions within a reasonable time as well as to obtain good exploration and exploitation of search space. The aim of presented study was to compare efficiency of different multi-objective algorithms in solving this task.

A new design method based on firefly algorithm for IIR system identification problem

In this paper a population based evolutionary optimization methodology called firefly algorithm (FFA) is applied for the optimization of system coefficients of the infinite impulse response (IIR) system identification problem. FFA is inspired by the flash pattern and characteristics of fireflies. In FFA technique, behaviour of flashing firefly towards its competent mate is structured. In this algorithm attractiveness depends on brightness of light and a bright firefly feels more attraction for the brighter one. For this optimization problem, brightness varies inversely proportional to the error fitness value, so the position of the brightest firefly gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic movement equation results in balancing of exploration and exploitation of search space. The proposed FFA based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using FFA over GA, PSO and DE in terms of convergence speed, identifying plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.

A novel design method for optimal IIR system identification using opposition based harmony search algorithm

In this paper a population based evolutionary optimization methodology called Opposition based Harmony Search Algorithm (OHS) is applied for the optimization of system coefficients of adaptive infinite impulse response (IIR) system identification problem. The original Harmony Search (HS) algorithm is chosen as the parent one and opposition based approach is applied to it with an intention to exhibit accelerated near global convergence profile. During the initialization, for choosing the randomly generated population/solution opposite solutions are also considered and the fitter one is selected as apriori guess for having faster convergence profile. Each solution in Harmony Memory (HM) is generated on the basis of memory consideration rule, a pitch adjustment rule and a re-initialization process which gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic HS algorithm results in balancing of exploration and exploitation of search space. The proposed OHS based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed OHS based system identification approach over GA, PSO and DE in terms of convergence speed, identifying the system plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.

Mixed-Integer Constrained Optimization Based on Memetic Algorithm

Evolutionary algorithms (EAs) are population-based global search methods. They have been successfully applied tomany complex optimization problems. However, EAs are frequently incapable of finding a convergence solution indefault of local search mechanisms. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators withlocal search methods. With global exploration and local exploitation in search space, MAs are capable of obtainingmore high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-basedsearch algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, amemetic algorithm based on MIHDE is developed for solving mixed-integer optimization problems. However, most ofreal-world mixed-integer optimization problems frequently consist of equality and/or inequality constraints. In order toeffectively handle constraints, an evolutionary Lagrange method based on memetic algorithm is developed to solvethe mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on twobenchmark mixed-integer constrained optimization problems. Experimental results show that the proposed algorithmcan find better optimal solutions compared with some other search algorithms. Therefore, it implies that the proposedmemetic algorithm is a good approach to mixed-integer optimization problems.

A Mixed-Integer Memetic Algorithm Applied to Batch Process Optimization

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a mixed-integer memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and applied to the optimal design of batch processes. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

A Memetic Algorithm for Mixed-Integer Optimization Problems

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on a benchmark mixed-integer constrained optimization problem. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

Efficient and Accurate Optimal Linear Phase FIR Filter Design Using Opposition-Based Harmony Search Algorithm

In this paper, opposition-based harmony search has been applied for the optimal design of linear phase FIR filters. RGA, PSO, and DE have also been adopted for the sake of comparison. The original harmony search algorithm is chosen as the parent one, and opposition-based approach is applied. During the initialization, randomly generated population of solutions is chosen, opposite solutions are also considered, and the fitter one is selected as a priori guess. In harmony memory, each such solution passes through memory consideration rule, pitch adjustment rule, and then opposition-based reinitialization generation jumping, which gives the optimum result corresponding to the least error fitness in multidimensional search space of FIR filter design. Incorporation of different control parameters in the basic HS algorithm results in the balancing of exploration and exploitation of search space. Low pass, high pass, band pass, and band stop FIR filters are designed with the proposed OHS and other aforementioned algorithms individually for comparative optimization performance. A comparison of simulation results reveals the optimization efficacy of the OHS over the other optimization techniques for the solution of the multimodal, nondifferentiable, nonlinear, and constrained FIR filter design problems.

An Annealed Genetic Algorithm for Multi Mode Resource Constrained Project Scheduling Problem

This paper presents a genetic algorithm (SA) for the classical multimode resource-constrained project scheduling problem (MRCPSP). The objective is makespan minimization of the project. A new precedence feasible multi point forward backward crossover operator is presented. SA and GA are used in tandem to ensure balance in exploration and exploitation of search space. SA is employed as local search procedure, due to its stochastic neighborhood selection strategy to escape local optima hence a good exploitation strategy; and GA as exploration strategy due to its large number of population. Computational investigations are carried out on standard data set problems from PSPLIB and it is proved that hybridization of GA with SA gives competitive performance when compared with currently available algorithms