scholarly journals Multiobjective Ant Lion Approaches Applied to Electromagnetic Device Optimization

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 35
Author(s):  
Juliano Pierezan ◽  
Leandro dos S. Coelho ◽  
Viviana C. Mariani ◽  
Sotirios K. Goudos ◽  
Achilles D. Boursianis ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Pareto Optimal Solutions ◽  
Selection Mechanism ◽  
Hunting Behavior ◽  
Powerful Approach ◽  
Motor Design ◽  
Tournament Selection ◽  
Ant Lion ◽  
Single Objective ◽  
Nature Inspired Metaheuristics

Nature-inspired metaheuristics of the swarm intelligence field are a powerful approach to solve electromagnetic optimization problems. Ant lion optimizer (ALO) is a nature-inspired stochastic metaheuristic that mimics the hunting behavior of ant lions using steps of random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. To extend the classical single-objective ALO, this paper proposes four multiobjective ALO (MOALO) approaches using crowding distance, dominance concept for selecting the elite, and tournament selection mechanism with different schemes to select the leader. Numerical results from a multiobjective constrained brushless direct current (DC) motor design problem show that some MOALO approaches present promising performance in terms of Pareto-optimal solutions.

scholarly journals Efficiency of MOMA-plus Method to Solve Some Fully Fuzzy L-R Triangular Multiobjective Linear Programs

2018 ◽  
Vol 10 (2) ◽  
pp. 77 ◽  
Author(s):  
Abdoulaye Compaoré ◽  
Kounhinir Somé ◽  
Joseph Poda ◽  
Blaise Somé
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Linear Programs ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Linear Optimization Problem ◽  
Novel Approach ◽  
Linear Optimization Problems ◽  
Single Objective

In this paper, we propose a novel approach for solving some fully fuzzy L-R triangular multiobjective linear optimization programs using MOMA-plus method (Kounhinir, 2017). This approach is composed of two relevant steps such as the converting of the fully fuzzy L-R triangular multiobjective linear optimization problem into a deterministic multiobjective linear optimization and the applying of the adapting MOMA-plus method. The initial version of MOMA-plus method is designed for multiobjective deterministic optimization (Kounhinir, 2017) and having already been tested on the single-objective fuzzy programs (Abdoulaye, 2017). Our new method allow to find all of the Pareto optimal solutions of a fully fuzzy L-R triangular multiobjective linear optimization problems obtained after conversion. For highlighting the efficiency of our approach a didactic numerical example is dealt with and obtained solutions are compared to Total Objective Segregation Method proposed by Jayalakslmi and Pandia (Jayalakslmi 2014).

Exploratory cuckoo search for solving single-objective optimization problems

2021 ◽  
Author(s):  
Bilal H. Abed-alguni ◽  
Noor Aldeen Alawad ◽  
Malek Barhoush ◽  
Rafat Hammad
Keyword(s):  
Optimization Problems ◽  
Cuckoo Search ◽  
Single Objective

Optimum Oil Production Planning Using Infeasibility Driven Evolutionary Algorithm

2013 ◽  
Vol 21 (1) ◽  
pp. 65-82 ◽  
Author(s):  
Hemant Kumar Singh ◽  
Tapabrata Ray ◽  
Ruhul Sarker
Keyword(s):  
Evolutionary Algorithm ◽  
Production Planning ◽  
Oil Recovery ◽  
Optimization Problems ◽  
Nonlinear Function ◽  
Practical Interest ◽  
Oil Production ◽  
Economic Benefits ◽  
Financial Benefit ◽  
Single Objective

In this paper, we discuss a practical oil production planning optimization problem. For oil wells with insufficient reservoir pressure, gas is usually injected to artificially lift oil, a practice commonly referred to as enhanced oil recovery (EOR). The total gas that can be used for oil extraction is constrained by daily availability limits. The oil extracted from each well is known to be a nonlinear function of the gas injected into the well and varies between wells. The problem is to identify the optimal amount of gas that needs to be injected into each well to maximize the amount of oil extracted subject to the constraint on the total daily gas availability. The problem has long been of practical interest to all major oil exploration companies as it has the potential to derive large financial benefit. In this paper, an infeasibility driven evolutionary algorithm is used to solve a 56 well reservoir problem which demonstrates its efficiency in solving constrained optimization problems. Furthermore, a multi-objective formulation of the problem is posed and solved using a number of algorithms, which eliminates the need for solving the (single objective) problem on a regular basis. Lastly, a modified single objective formulation of the problem is also proposed, which aims to maximize the profit instead of the quantity of oil. It is shown that even with a lesser amount of oil extracted, more economic benefits can be achieved through the modified formulation.

scholarly journals An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1457
Author(s):  
Avelina Alejo-Reyes ◽  
Erik Cuevas ◽  
Alma Rodríguez ◽  
Abraham Mendoza ◽  
Elias Olivares-Benitez
Keyword(s):  
Supplier Selection ◽  
Search Strategy ◽  
Optimization Problems ◽  
Displacement Vector ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Order Quantity ◽  
Hunting Behavior

Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

An Interactive Neural Network for Constrained Multi-Objective Optimization with Application to the Design of Digital Filters

2012 ◽  
Vol 433-440 ◽  
pp. 2808-2816
Author(s):  
Jian Jin Zheng ◽  
You Shen Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Complexity ◽  
Digital Filters ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective

This paper presents a new interactive neural network for solving constrained multi-objective optimization problems. The constrained multi-objective optimization problem is reformulated into two constrained single objective optimization problems and two neural networks are designed to obtain the optimal weight and the optimal solution of the two optimization problems respectively. The proposed algorithm has a low computational complexity and is easy to be implemented. Moreover, the proposed algorithm is well applied to the design of digital filters. Computed results illustrate the good performance of the proposed algorithm.

scholarly journals A Quantum Adiabatic Algorithm for Multiobjective Combinatorial Optimization

Axioms ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 32 ◽  
Author(s):  
Benjamín Barán ◽  
Marcos Villagra
Keyword(s):  
Combinatorial Optimization ◽  
Multiobjective Optimization ◽  
Theorem Proving ◽  
Optimization Problems ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Quantum Annealing ◽  
Multiobjective Optimization Problems ◽  
First Time

In this work we show how to use a quantum adiabatic algorithm to solve multiobjective optimization problems. For the first time, we demonstrate a theorem proving that the quantum adiabatic algorithm can find Pareto-optimal solutions in finite-time, provided some restrictions to the problem are met. A numerical example illustrates an application of the theorem to a well-known problem in multiobjective optimization. This result opens the door to solve multiobjective optimization problems using current technology based on quantum annealing.

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