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Modified Nelder-Mead self organizing migrating algorithm for function optimization and its application

APPLIED SOFT COMPUTING 2017年 51卷 341-350页

作者： Agrawal, Seema Singh, Dipti Gautam Buddha Univ Dept Math Greater Noida India CCS Univ Dept Math SSV Coll Meerut Uttar Pradesh India

In this paper, a modified Nelder Mead self organizing migrating algorithm (mNM-SOMA) has been presented for solving unconstrained optimization problems. It is based on the hybridization of self organizing migrating algorithm (SOMA) with modified Nelder Mead (mNM) Crossover Operator. SOMA is a low population based technique that has good exploration and exploitation qualities, but sometimes converges premature to local optima solution due to lack of diversity preserve mechanism. In this paper an attempt has been made to improve the efficiency of SOMA using a modified NM crossover operator( mNM) for maintaining the diversity in the search space. mNM-SOMA has been tested on a set of 15 test problems, taken form literature and results are compared with the results obtained by self organizing migrating genetic algorithm (SOMGA), SOMA, genetic algorithm (GA) and particle swarm optimization( PSO). For better presentation, results are also analyzed graphically using a Performance Index. Besides this, mNM-SOMA has also been used to solve Frequency Modulation Sounds Parameter Identification Problem. Analysis of numerical results infers mNM-SOMA as a less expensive robust technique. (C) 2016 Elsevier B.V. All rights reserved.

关键词： self organizing migrating algorithm Nelder Mead crossover operator Genetic algorithm Particle swarm optimization Function optimization Hybridizationa

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self organizing migrating algorithm with quadratic interpolation for solving large scale global optimization problems

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APPLIED SOFT COMPUTING 2016年 38卷 1040-1048页

作者： Singh, Dipti Agrawal, Seema Gautam Buddha Univ Dept Appl Sci Greater Noida India CCS Univ SSV PG Coll Dept Math Hapur India

Generally the complexity of the large scale optimization problem is considered to increase as the size or dimension of the problem increases and to solve these problems;more efficient and robust algorithms are needed. Several experiments have shown that an increment in dimensions of the problem not only requires an increment in population size but increases the computational cost also. In this paper a self organizing migrating algorithm with Quadratic Interpolation (SOMAQI) has been extended to solve large scale global optimization problems for dimensions ranging from 100 to 3000 with a constant population size of 10 only. It produces high quality optimal solution with very low computational cost and converges very fast to optimal solution. (C) 2015 Elsevier B.V. All rights reserved.

关键词： self organizing migrating algorithm Quadratic interpolation Large scale global optimization

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Opportunistic self organizing migrating algorithm for Real-Time Dynamic Traveling Salesman Problem 51st

Opportunistic Self Organizing Migrating Algorithm for Real-T...

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51st Annual Conference on Information Sciences and Systems (CISS)

作者： Dokania, Shubham Bagga, Sunyam Sharma, Rohit Delhi Technol Univ Dept Comp Sci New Delhi India Delhi Technol Univ Dept Appl Math New Delhi India

ISBN: (纸本)9781509047802

self organizing migrating algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic self organizing migrating algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.

关键词： Dynamic Traveling Salesman Problem Evolutionary algorithms Optimization self organizing migrating algorithm

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Cost Optimization of 2-Way Ribbed Slab Using Hybrid self organizing migrating algorithm 1

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6th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Vidyarthi, Piyush Singh, Dipti Pal, Shilpa Agrawal, Seema Gautam Buddha Univ Dept Civil Engn Greater Noida Uttar Pradesh India Gautam Buddha Univ Greater Noida Uttar Pradesh India SSVPG Coll Dept Math Hapur Uttar Pradesh India

ISBN: (数字)9789811033254

ISBN: (纸本)9789811033254;9789811033247

In this paper, an optimization problem of 2-way ribbed slab or waffle square slab has been solved using a hybrid variant of self organizing migrating algorithm, C-SOMAQI. The main objective of this problem is to design a 2-way ribbed waffled slab of dimension 10 m * 10 m with optimum cost of steel as well as concrete. The design variables of 2-way ribbed waffled slab are taken as the effective depth of the slab (j), ribs width (p), the spacing between ribs (b), effective depth of ribs (d), and the area of reinforcement. Various population based techniques are available in literature to solve optimization problems. In this paper, a low population based hybrid technique C-SOMAQI has been taken to solve this problem that provides the solution at an optimum cost. To validate the claim, this problem has also been solved theoretically and a comparative analysis between these two solutions has been made. The study concludes that C-SOMAQI provides better results as compare to theoretical method.

关键词： Ribbed slab Waffle slab Optimization self organizing migrating algorithm

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Brain Storm Optimization algorithm in Objective Space for Reliability-Redundancy Allocation Problem

Brain Storm Optimization Algorithm in Objective Space for Re...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Nath, Rahul Rauniyar, Amit Muhuri, Pranab K. South Asian Univ Dept Comp Sci New Delhi 110021 India

ISBN: (纸本)9781728121536

This paper deals with one of the well-studied research problem known as reliability redundancy allocation problem (RRAP). RRAP is finds an optimal balance in selecting reliability of the components and the number of redundant components considering the effect of cost, weight, and volume on the overall system as constraints. Reliability is a very crucial issue in every engineering application. In this paper, our study aims to find the optimal layout for RRAP using brain storm optimization algorithm in objective space (BSO-OS). BSO is inspired by human cognitive skills. The convergent and divergent operation to reduce the search space and diversification of population are the prime objective in BSO. Results are compared by solving the RRAP with the self-organizing migrating algorithm (SOMA) and the genetic algorithm (GA). It is found that BSO-OS outperform others in terms of time complexity providing similar results.

关键词： Reliability redundancy allocation problem Brain storm optimization algorithm Objective space self organizing migrating algorithm Genetic algorithm

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Log-Logistic SOMA with Quadratic Approximation Crossover

Log-Logistic SOMA with Quadratic Approximation Crossover

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International Conference on Computing, Communication & Automation (ICCCA)

作者： Singh, Dipti Agrawal, Seema Gautam Buddha Univ Dept Appl Sci Greater Noida India SSVPG Coll Dept Math Hapur India

ISBN: (纸本)9781479988907

Though population based algorithms performs well to solve many global optimization problems, many attempts has been made in literature to improve the efficiency of these algorithms. One possible way is to hybridized them with the features of other deterministic or population based techniques. This Paper presents a Log-Logisticself organizing migrating algorithm with quadratic approximation crossover (LLSOMAQI). This algorithm is an extension of algorithms SOMAQI, in which self organizing migrating algorithm (SOMA) has been hybridized with quadratic approximation (QA) crossover operator and SOMA-M, which is hybridization of SOMA and Log-Logistic (LL) mutation. LLSOMAQI has been tested on 15 benchmark unconstrained test problems and an analysis has been made between the three algorithms. LLSOMAQI, its originator SOMA and PSO to show the efficiency of this algorithm over other population based algorithms.

关键词： self organizing migrating algorithm Quadratic approximation crossover operator Log-logistic mutation operator Particle swarm optimization Global Optimization

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A Hybrid self organizing migrating algorithm for Solving Fourth-Party Logistics Routing Problem with Fuzzy Duration Time

A Hybrid Self Organizing Migrating Algorithm for Solving Fou...

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第三届中国智能计算大会

作者： Yan Cui~(1,2)Min Huang~(1,2)Xingwei Wang~1 1.College of Information Science and Engineering,Northeastern University,Shenyang,Liaoning,110004,China 2.Key Laboratory of Integrated Automation of Process Industry(Northeastern University),Ministry of Education, Shenyang,Liaoning,110004,China

From the beginning of the 21century, fourth party logistics（4PL）has been attracting more and more attention in the field of academic and *** this paper,a 4PL routing problem（4PLPR） with fuzzy duration time is presented,and the fuzzy numbers is used to denote the uncertainty of duration *** a simple description of it,an integer programming for the problem is *** a hybrid self organizing migrating algorithm is designed.A high adaptive length-variant coding method formed by two arrays stand together is used to represent the individual where the individual corresponds to a path as a *** such individuals to form the initial group by Kth shortest *** migrating,best individual in the current generation is selected according to the fitness ***,an extensive computational analysis is presented and the numerical results show that which route should be selected in order to get minimum cost in the due date.

关键词： fourth party logistics routing problem fuzzy duration time self organizing migrating algorithm Kth shortest path algorithm

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A Hybrid self organizing migrating algorithm for Solving Fourth-Party Logistics Routing Problem with Fuzzy Duration Time

A Hybrid Self Organizing Migrating Algorithm for Solving Fou...

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第三届中国智能计算大会

作者： Yan Cui Min Huang Xingwei Wang College of Information Science and Engineering Northeastern University.ShenyangLiaoning110004Chin College of Information Science and Engineering Northeastern University.ShenyangLiaoning110004Chin

From the beginning of the 21st century,fourth party logistics(4PL)has been attracting more and more attention in the field of academic and *** this paper,a 4PL routing problem(4PLPR)with fuzzy duration time is presented,and the fuzzy numbers is used to denote the uncertainty of duration *** a simple description of it,an integer programming for the problem is *** a hybrid self organizing migrating algorithm is designed.A high adaptive length-variant coding method formed by two arrays stand together is used to represent the individual where the individual corresponds to a path as a *** such individuals to form the initial group by Kth shortest *** migrating,best individual in the current generation is selected according to the fitness ***,an extensive computational analysis is presented and the numerical results show that which route should be selected in order to get minimum cost in the due date.

关键词： fourth party logistics routing problem fuzzy duration time self organizing migrating algorithm Kth shortest path algorithm

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