Discrete Imperialist Competitive Algorithm Research Articles

Optimization of product line considering compatibility and reliability via discrete imperialist competitive algorithm

In the era of mass customization, designing optimal products is one of the most critical decision-making for a company to stay competitive. More and more customers like customized products, which will bring challenges to the product line design and the production. If a company adopts customers’ favorite levels, this may lead to lower product reliability, or incompatibility among the components that make up the product. Moreover, it is worth outsourcing certain attribute levels to reduce production cost, but customers may dislike these levels because of their delivery delay. If managers consider the compatibility issue, the quality issue, outsource determination, and the delivery due date in the product design and production stages, they will avoid unreasonable product configuration and many unnecessary expenses, thereby bringing benefits to the company. To solve this complicated problem, we establish a nonlinear program that maximizes Per-capita-contribution Margin considering Reliability Penalty. Since the integrated product line design and production problem is NP-hard, we propose an improved Discrete Imperialist Competitive Algorithm (DICA). The proposed DICA is compared with genetic algorithm (GA) and simulated annealing (SA) through extensive numerical experiment, and the results show that DICA displays 6%~17% and 5%~14% improvement over GA and SA in terms of solution quality, respectively.

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem with energy consumption

The resource-constrained hybrid flowshop problem (RCHFS) has been investigated thoroughly in recent years. However, the practical case that considers both resource-constrained and energy consumption still has rare research. To address this issue, a discrete imperialist competitive algorithm (DICA) was proposed to minimize the makespan and energy consumption. In the proposed algorithm, first, each solution was represented by a two-dimensional vector, where one vector represented the scheduling sequence and another one showed the machine assignment. Then, a decoding method considering the resource allocation was designed. Finally, we combined DICA and the simulated annealing algorithm (SA) to improve the performance of the proposed approach. Furthermore, we tested the proposed algorithm based on a randomly generated set of real shop scheduling system instances and compared with the existing heuristic algorithms. The results confirmed that the proposed algorithm can solve the RCHFS with high efficiency.

Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem

ABSTRACT The resource constrained scheduling problem has been investigated widely in recent years, and many heuristic algorithms have been applied to solve this problem. In this study, we propose a discrete imperialist competitive algorithm (DICA) to solve a variety of resource-constrained hybrid flowshop scheduling problems with the objective of minimizing the completion time. In the proposed algorithm, we employ a two-phase-based coding mechanism, where a local search method is applied. Then, we combine DICA and simulated annealing algorithm (SA) to improve the performance of the algorithm. In addition, we consider the dynamic allocation of resources in the decoding process. We tested the proposed algorithm based on a randomly generated set of real shop scheduling system instances, as well as numerically analyzing and comparing the proposed algorithm with existing heuristic algorithms to verify its effectiveness.

Reconfiguration of Virtual Cellular Manufacturing Systems via Improved Imperialist Competitive Approach

This paper constructs an integrated virtual reconfiguration model that can simultaneously group workstations, schedule virtual cells, and select energy consumption levels. If managers prefer the physical proximity of machines in a certain virtual cell, the material handling cost in it can be reduced. However, the distances among machines in other virtual cells are probably large, which may cause greater material handling cost. If a virtual cell for a certain product type has priority to be created, the backorder cost of that type can be decreased or even avoided. Nevertheless, the creation of virtual cells for other product types may be delayed, perhaps leading to the higher backorder cost of other product types. In addition, managers can choose a high energy consumption level of a machine to expedite its production to reduce backorder cost at the expense of more energy consumption cost. To minimize the total operational cost, we develop a novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic (DICAP). It includes a colony movement strategy, a competition strategy, a collapse mechanism, a development strategy, and a sufficient convergence policy. Numerical experiments and t-test are conducted to validate that the proposed DICAP outperforms genetic algorithm and simulated annealing. Note to Practitioners —Virtual cellular manufacturing systems can create an expectation of improving machine utilization and productivity, reducing reconfiguration cost, and adaptation to product specification changes in reality. However, managers often feel difficult to make appropriate decisions for three interrelated issues, i.e., workstation grouping, virtual cell creation and release, and energy consumption options. Workstation grouping depends on the availability of machines and workers, and the availability may be related to the creation and release time of some virtual cells. Whether a virtual cell is created in time has an influence on the selection of energy consumption levels, because managers need to consider both backorder cost and energy consumption cost. In addition, there often exists a bottleneck workstation in each virtual cell. Therefore, it is essential and worthwhile to select appropriate energy consumption levels of machines to smooth the production efficiency of grouped workstations. To effectively address the issues, this paper presents an integrated virtual reconfiguration model. A novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic is developed to minimize the total operational cost. Numerical experiments and t-test results indicate that the proposed algorithm outperforms two commonly used ones, i.e., genetic algorithm and simulated annealing in solution quality. It is suitable for virtual reconfiguration problem with industrial size in practice.

Human resource allocation in an emergency department

Purpose The complexity and interdisciplinarity of healthcare industry problems make this industry one of the attention centers of computer-based simulation studies to provide a proper tool for interaction between decision-makers and experts. The purpose of this study is to present a metamodel-based simulation optimization in an emergency department (ED) to allocate human resources in the best way to minimize door to doctor time subject to the problem constraints which are capacity and budget. Design/methodology/approach To obtain the objective of this research, first the data are collected from a public hospital ED in Brazil, and then an agent-based simulation is designed and constructed. Afterwards, three machine-learning approaches, namely, adaptive neuro-fuzzy inference system (ANFIS), feed forward neural network (FNN) and recurrent neural network (RNN), are used to build an ensemble metamodel through adaptive boosting. Finally, the results from the metamodel are applied in a discrete imperialist competitive algorithm (ICA) for optimization. Findings Analyzing the results shows that the yellow zone section is considered as a potential bottleneck of the ED. After 100 executions of the algorithm, the results show a reduction of 24.82 per cent in the door to doctor time with a success rate of 59 per cent. Originality/value This study fulfils an identified need to optimize human resources in an ED with less computational time.

A Discrete Imperialist Competitive Algorithm for WSN Deployment

The WSN deployment problem is addressed in this paper. The problem applies to the monitored areas with different detection needs at different points. In this problem, every point of the terrain is assigned with a predefined minimum probability of event detection. The objective is providing the best position for the network nodes and at the same time assuring event detection, detection message delivery, and reducing deployment cost. We have formulated the problem as an optimization problem with three objectives, which is NP-complete. Because of the huge solution space for the problem and the exponential computational complexity, none of the exact methods known yet can solve the problem unless for a pretty small scaled case. To battle the complexity of the solution, a new scalable solution is proposed based on imperialist competitive algorithm namely imperialist competitive deployment algorithm (ICDA). We compare the proposal to the related deployment strategies, and the results show that ICDA outperforms them.

Design for a Crane Metallic Structure Based on Imperialist Competitive Algorithm and Inverse Reliability Strategy

Uncertainties in parameters such as materials, loading, and geometry are inevitable in designing metallic structures for cranes. When considering these uncertainty factors, reliability-based design optimization (RBDO) offers a more reasonable design approach. However, existing RBDO methods for crane metallic structures are prone to low convergence speed and high computational cost. A unilevel RBDO method, combining a discrete imperialist competitive algorithm with an inverse reliability strategy based on the performance measure approach, is developed. Application of the imperialist competitive algorithm at the optimization level significantly improves the convergence speed of this RBDO method. At the reliability analysis level, the inverse reliability strategy is used to determine the feasibility of each probabilistic constraint at each design point by calculating its α-percentile performance, thereby avoiding convergence failure, calculation error, and disproportionate computational effort encountered using conventional moment and simulation methods. Application of the RBDO method to an actual crane structure shows that the developed RBDO realizes a design with the best tradeoff between economy and safety together with about one-third of the convergence speed and the computational cost of the existing method. This paper provides a scientific and effective design approach for the design of metallic structures of cranes.

Feature Selection by Using Discrete Imperialist Competitive Algorithm to Spam Detection

Spam is a basic problem in electronic communications such as email systems in large scales and large number of weblogs and social networks. Due to the problems created by spams, much research has been carried out in this regard by using classification techniques. Redundant and high dimensional information are considered as a serious problem for these classification algorithms due to their high computation costs and using a memory. Reducing feature space results in representing an understandable model and using various methods. In this paper, the method of feature selection by using imperialist competitive algorithm has been presented. Decision tree and SVM classifications have been taken into account in classification phase. In order to prove the efficiency of this method, the results of evaluating data set of Spam Base have been compared with the algorithms proposed in this regard such as genetic algorithm. The results show that this method improves the efficiency of spam detection.

Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem

The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete imperialist competitive algorithm in this paper. The proposed algorithm modifies the original rules of the assimilation and introduces the 2-opt algorithm into the revolution process. To examine its performance, we tested the proposed algorithm on 10 small-scale and 2 large-scale standard benchmark instances from the TSPLIB and compared the experimental results with that obtained by two other ICA-based algorithms and six other existing algorithms. The proposed algorithm shows excellent performance in the experiments and comparisons.

A weighted discrete imperialist competitive algorithm (WDICA) combined with chaotic map for image encryption

In recent years, there has been increasing interest in the security of digital images. This study focuses on binary image encryption using the weighted discrete imperialist competitive algorithm (WDICA).In the proposed method, a chaotic map is first used to create a specified number of cipher images. Then, to improve the results, WDICA is applied to the cipher images. In this study, entropy and correlation coefficient are used as WDICA's fitness functions. The goal is to maximize the entropy and minimize correlation coefficients. The advantage of this method is its ability to optimize the outcome of all iterations using WDICA. Simulation results show that WDICA not only demonstrates excellent encryption but also resists various typical attacks. The obtained correlation coefficient and entropy of the proposed WDICA are approximately 0.004 and 7.9994, respectively.