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Approaches to Integrate Process Planning and Scheduling
Published in Rakesh Kumar Phanden, Ajai Jain, J. Paulo Davim, Integration of Process Planning and Scheduling, 2019
Rakesh Kumar Phanden, Ajai Jain
Li et al. (2010a) presented an IPPS solution for job shop environment through a mathematical model and three agents, namely, job agent, machine agent, and an optimisation agent, along with databases. They considered processing, sequential, and operational manufacturing flexibilities during process planning function, and optimised for production time. The scheduling objective was makespan. The authors concluded that the proposed approach performed rescheduling through the machine agent’s negotiation with job agents and optimisation agents whenever the changes occurred at the shop floor. Lian et al. (2012) proposed population-based evolutionary algorithm, called Imperialist Competitive Algorithm (ICA), to optimise makespan of the IPPS problem. The solution (schedule plan and process plan) was encoded corresponding to AND/OR graph information that composed of scheduling string, operation sequence string, machines sequence string, and OR-connectors string for each job. Hence, the schedule plan and process plan for each job were generated concurrently and explored various manufacturing flexibility. The authors elaborated various steps of ICA, viz., assimilation, imperialistic competition, revolution, and elimination with an example. They conclude that ICA outperformed the SEA and GA approaches.
A Study on Metaheuristic-Based Neural Networks for Image Segmentation Purposes
Published in Qurban A. Memon, Shakeel Ahmed Khoja, Data Science, 2019
Navid Razmjooy, Vania V. Estrela, Hermes J. Loschi
The Imperialist Competitive Algorithm (ICA) algorithm is one of the newest intelligent optimization algorithms that have been introduced in the field of artificial intelligence and metaheuristic algorithms [75–77]. The main reason for this algorithm is to simulate the colonial political process. In the same way that the GA simulates biological evolution, in the imperialist competition algorithm, political evolution has been used. This algorithm was introduced in 2007 by Atashpaz-Gargari and Lucas and has been ever since used as a tool for many applications and research fields. The high power of this algorithm, especially in dealing with continuous issues, has led the imperialist competition algorithm to be considered as one of the major tools for optimization [78].
Dialectics of Nature: Inspiration for Computing
Published in Nazmul Siddique, Hojjat Adeli, Nature-Inspired Computing, 2017
A society can be seen as a collection of individuals in the parametric space. There are leaders in the society who are the best performing individuals that help others to improve through information exchange. Daneshyari and Yen (2004) proposed a novel optimization strategy based on a social algorithm and collective behaviors. The new algorithm incorporates the information of the individuals within the society introduced as their talent and the collective behavior of the society in the civilization called the liberty rate. Atashpaz-Gargari and Lucas (2007) proposed an algorithm that has been inspired by evolutionary development of human society. This algorithm has looked at the political process as a process of human sociopolitical evolution. They formulated a social-based algorithm by combining the evolutionary progress and the sociopolitical process, resulting in the imperialist competitive algorithm (ICA). People live in different types of communities, which leads to different styles of leadership development. This approach tries to capture several people involved in the characteristic of community development (Ramezani and Lotfi, 2013).
A New Method to Determine the Optimal Location and Amount of Shed Load in Multi-Stage Under Frequency Load Shedding Using DIgSILENT
Published in Electric Power Components and Systems, 2022
Javad Moeini Hadizadeh, Mahdi Samadi, Mohammad Ebrahim Hajiabadi
The imperialist competitive algorithm (ICA) is used for optimization. The ICA is a population-based algorithm that starts with a number of countries as primary answers [32]. The repetition trend of this algorithm is such that the unwanted solutions are removed and the better solutions will remain, and after the convergence process, the optimal universal answer to the problem will be determined. The process of optimization and exchange between two MATLAB and DIgSILENT software is illustrated in Figure 3. DIgSILENT software is called and run once to calculate the fitness (value of the objective function) of each of the solutions. In this calling:
Determination of gold in water samples using electromembrane extraction
Published in Journal of Dispersion Science and Technology, 2018
Mostafa Khajeh, Maryam Dahmardeh, Mousa Bohlooli, Ali Khatibi, Mansour Ghaffari-Moghaddam
Imperialist competitive algorithm (ICA) is a computational procedure, and its meta-heuristic algorithms suggested solving optimization problems of various types. The ICA works according to the socio-politically inspired optimization technique. This algorithm shows a good convergence rate to reach a global optimum for different optimization problems.[13,14]