Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems

Mirjalili, Seyedali; Jangir, Pradeep; Saremi, Shahrzad

doi:10.1007/s10489-016-0825-8

Cited by 576 publications

(212 citation statements)

References 42 publications

Supporting

Mentioning

186

Contrasting

Order By: Relevance

“…where cumsum is cumulative sum, t is the random walk step, n is the number of iterations and r (t) is a stochastic function [35]. The random walk is normalized as follows:…”

Section: Ant Lion Optimizermentioning

confidence: 99%

See 1 more Smart Citation

Single and multi-objective operation management of micro-grid using krill herd optimization and ant lion optimizer algorithms

Fathy

Abdelaziz

2018

Int J Energy Environ Eng

View full text Add to dashboard Cite

In this paper, two recent heuristic optimization algorithms are presented to optimally manage the operation of the microgrid (MG) with installed renewable energy sources (RESs); krill herd (KH) optimization and ant lion optimizer (ALO) algorithms. The first algorithm is used for solving single-objective function represents either total operation cost or total pollutant emission injected from the installed generating units while ALO is applied to solve the multi-objective function of both total operating cost and emission. The problem is formulated as nonlinear constrained objective function with equality and inequality constraints. In this work; the devices installed in MGs are photovoltaic panel (PV), wind turbine (WT), microturbine (MT), fuel cell (FC), battery and grid. Two scenarios are studied; the first one is optimizing MG with installing all RESs within specified limits in addition to grid, while the second scenario is operating both PV and WT at their rated powers. The obtained results are compared with different reported algorithms like genetic algorithm (GA), Fuzzy self-adaptive PSO (FSAPSO) and others programmed like particle swarm optimization (PSO), grey-wolf optimizer (GWO) and whale optimization algorithm (WOA). For first scenario; the proposed KH gives the best optimal cost of 105.94 €ct while the best emission is 420.57 kg, the best optimal cost and emission of 592.86 €ct 339.71 kg are obtained via KH in the second scenario.

show abstract

“…where cumsum is cumulative sum, t is the random walk step, n is the number of iterations and r (t) is a stochastic function [35]. The random walk is normalized as follows:…”

Section: Ant Lion Optimizermentioning

confidence: 99%

“…The KH is used for solving single-objective optimization problem while ALO is used to solve multi-objective one. ALO is selected for solving the multi-objective problem due to high convergence and coverage in obtaining Pareto optimal solution [35].…”

Section: Ant Lion Optimizermentioning

confidence: 99%

Single and multi-objective operation management of micro-grid using krill herd optimization and ant lion optimizer algorithms

Fathy

Abdelaziz

2018

Int J Energy Environ Eng

View full text Add to dashboard Cite

show abstract

“…It has two important characteristics: group search strategy and information exchange among individuals. Multi objective evolutionary algorithm compared with the traditional method, its advantage lies in: in the implementation of a algorithm to obtain a set of optimal solutions, to avoid most of the traditional methods need to run multiple times and bring huge time overhead; the algorithm is not affected by the application of Pareto optimal front shape; evolutionary algorithm used to evaluate the objective function of the individual advantages and disadvantages of different problem solving, you only need to design the corresponding fitness function, without modifying the other part of the algorithm has good universality [9,10]. At present, many efficient multi-objective evolutionary algorithms have been widely used in engineering practice, production and life.…”

Section: Fitness Evaluation and Selection Strategy In Multi-objectivementioning

confidence: 99%

Research on Key Problems of Heuristic Evolutionary Algorithm for Dynamic High Dimensional Multi-Objective Optimization Problem

Yang¹,

Wang²

2018

Proceedings of the 2018 8th International Conference on Social Science and Education Research (SSER 2018)

View full text Add to dashboard Cite

Abstract. The multi-objective optimization method has a strong background of engineering application. In recent years, the problem of solving multi-objective optimization based on evolutionary algorithm has become a hot topic in the international academic circle. This paper studies the problem of dynamic application of high-dimensional multi-objective optimization problem heuristic evolutionary algorithm key technology and algorithm, which constructs online target dimension reduction algorithm model, alternating between the executive, the original problem is gradually transformed into smaller problems, improve the optimization algorithm, which provides a new method for solving high dimension the multi-objective optimization problem.

show abstract

“…It is worth mentioning that there are many methods to improve or optimize the weights of the neural networks, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), etc. [37][38][39]. In this paper, we choose Bayesian regularization mainly because the data in our study are scarce, and the Bayesian method can improve the performance of neural networks (by reducing the training iterations) [36].…”

Section: Training Parameter Selection On the Bp Neural Networkmentioning

confidence: 99%

A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

2017

View full text Add to dashboard Cite

Abstract:Air pollution has become one of the key environmental concerns in the urban sustainable development. It is important to evaluate the impact of air pollution on socioeconomic development since it is the prerequisite to enforce an effective prevention policy of air pollution. In this paper, we model the impact of air pollution on the urban economic development as a Multiple Criteria Decision Making (MCDM) problem. In particular, we propose a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis framework to evaluate multiple factors of air pollutants and economic development. Our method can overcome the drawbacks of conventional TOPSIS methods by using Bayesian regularization and the Back-Propagation (BP) neural network to optimize the weight training process. We have conducted a case study to evaluate our proposed framework.

show abstract

Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems

Cited by 576 publications

References 42 publications

Single and multi-objective operation management of micro-grid using krill herd optimization and ant lion optimizer algorithms

Single and multi-objective operation management of micro-grid using krill herd optimization and ant lion optimizer algorithms

Research on Key Problems of Heuristic Evolutionary Algorithm for Dynamic High Dimensional Multi-Objective Optimization Problem

A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

Contact Info

Product

Resources

About