Journal ArticleDOI
Grasshopper optimization algorithm for multi-objective optimization problems
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A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone and then a mechanism is proposed to use the model in approximating the global optimum in a single-objective search space.Abstract:
This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution.read more
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Journal ArticleDOI
RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method
Iman Ahmadianfar,Ali Asghar Heidari,Ali Asghar Heidari,Amir H. Gandomi,Xuefeng Chu,Huiling Chen +5 more
TL;DR: This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics.
Journal ArticleDOI
Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems
Majdi Mafarja,Ibrahim Aljarah,Ali Asghar Heidari,Abdelaziz I. Hammouri,Hossam Faris,Ala' M. Al-Zoubi,Seyedali Mirjalili +6 more
TL;DR: The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends.
Journal ArticleDOI
An improved grasshopper optimization algorithm with application to financial stress prediction
TL;DR: The improved GOA which combines three strategies to achieve a more suitable balance between exploitation and exploration was established and the proposed learning scheme can guarantee a more stable kernel extreme learning machine model with higher predictive performance compared to others.
Journal ArticleDOI
Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm
Ibrahim Aljarah,Ala' M. Al-Zoubi,Hossam Faris,Mohammad A. Hassonah,Seyedali Mirjalili,Heba Saadeh +5 more
TL;DR: A hybrid approach based on the Grasshopper optimisation algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarms of grasshoppers, is proposed to optimize the parameters of the SVM model, and locate the best features subset simultaneously.
Journal ArticleDOI
Golden eagle optimizer: A nature-inspired metaheuristic algorithm
TL;DR: A nature-inspired swarm-based metaheuristic for solving global optimization problems called Golden Eagle Optimizer (GEO), which shows GEO’s superiority, which indicates that it can find the global optimum and avoid local optima effectively.
References
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Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
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No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
Book
Ant Colony Optimization
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.