scholarly journals FLC control for tuning exploration phase in bio-inspired metaheuristic

2017 ◽  
Vol 16 (2) ◽  
pp. 32
Author(s):  
Kazimierz Kiełkowicz ◽  
Damian Grela
Keyword(s):  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Bat Algorithm ◽  
Search Space ◽  
Search Process ◽  
Control Parameters ◽  
Local Search Methods ◽  
Multidimensional Search ◽  
Exploration Phase

<p>Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey.</p>

Development and research of a genetic method for the analysis and determination of the location of power grid objects

2020 ◽  
Vol 25 (1) ◽  
pp. 20-42
Author(s):  
Fedorchenko I. ◽  
◽  
Oliinyk A. ◽  
Korniienko S. ◽  
Kharchenko A. ◽  
...  
Keyword(s):  
Power Supply ◽  
Narrow Range ◽  
Search Space ◽  
Distribution Networks ◽  
Power Supplies ◽  
Search Process ◽  
Stable Convergence ◽  
Genetic Method

The problem of combinatorial optimization is considered in relation to the choice of the location of the location of power supplies when solving the problem of the development of urban distribution networks of power supply. Two methods have been developed for placing power supplies and assigning consumers to them to solve this problem. The first developed method consists in placing power supplies of the same standard sizes, and the second - of different standard sizes. The fundamental difference between the created methods and the existing ones is that the proposed methods take into account all the material of the problem and have specialized methods for coding possible solutions, modified operators of crossing and selection. The proposed methods effectively solve the problem of low inheritance, topological unfeasibility of the found solutions, as a result of which the execution time is significantly reduced and the accuracy of calculations is increased. In the developed methods, the lack of taking into account the restrictions on the placement of new power supplies is realized, which made it possible to solve the problem of applying the methods for a narrow range of problems. A comparative analysis of the results obtained by placing power supplies of the same standard sizes and known methods was carried out, and it was found that the developed method works faster than the known methods. It is shown that the proposed approach ensures stable convergence of the search process by an acceptable number of steps without artificial limitation of the search space and the use of additional expert information on the feasibility of possible solutions. The results obtained allow us to propose effective methods to improve the quality of decisions made on the choice of the location of power supply facilities in the design of urban electrical.

An exploration-enhanced elephant herding optimization

2019 ◽  
Vol 36 (9) ◽  
pp. 3029-3046 ◽  
Author(s):  
Islam A. ElShaarawy ◽  
Essam H. Houssein ◽  
Fatma Helmy Ismail ◽  
Aboul Ella Hassanien
Keyword(s):  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Search Space ◽  
Exploration And Exploitation ◽  
Content Type ◽  
Discrepancy Measure ◽  
The One ◽  
Star Discrepancy ◽  
Exploration Phase

Purpose The purpose of this paper is to propose an enhanced elephant herding optimization (EEHO) algorithm by improving the exploration phase to overcome the fast-unjustified convergence toward the origin of the native EHO. The exploration and exploitation of the proposed EEHO are achieved by updating both clan and separation operators. Design/methodology/approach The original EHO shows fast unjustified convergence toward the origin specifically, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms. Furthermore, the star discrepancy measure is adopted to quantify the quality of the exploration phase of evolutionary algorithms in general. Findings In experiments, EEHO has shown a better performance of convergence rate compared with the original EHO. Reasons behind this performance are: EEHO proposes a more exploitative search method than the one used in EHO and the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Operator γ is added to EEHO assists to escape from local optima, which commonly exist in the search space. The proposed EEHO controls the convergence rate and the random walk independently. Eventually, the quantitative and qualitative results revealed that the proposed EEHO outperforms the original EHO. Research limitations/implications Therefore, the pros and cons are reported as follows: pros of EEHO compared to EHO – 1) unbiased exploration of the whole search space thanks to the proposed update operator that fixed the unjustified convergence of the EHO toward the origin and the proposed separating operator that fixed the tendency of EHO to introduce new elephants at the boundary of the search space; and 2) the ability to control exploration–exploitation trade-off by independently controverting the convergence rate and the random walk using different parameters – cons EEHO compared to EHO: 1) suitable values for three parameters (rather than two only) have to be found to use EEHO. Originality/value As the original EHO shows fast unjustified convergence toward the origin specifically, the search method adopted in EEHO is more exploitative than the one used in EHO because of the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Further, the star discrepancy measure is adopted to quantify the quality of exploration phase of evolutionary algorithms in general. Operator γ that added EEHO allows the successive local and global searching (exploration and exploitation) and helps escaping from local minima that commonly exist in the search space.

scholarly journals A Novel Hybrid Self-Adaptive Bat Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Iztok Fister ◽  
Simon Fong ◽  
Janez Brest ◽  
Iztok Fister
Keyword(s):  
Optimization Problems ◽  
Bat Algorithm ◽  
Search Space ◽  
Combinatorial Problems ◽  
Control Parameters ◽  
Search Heuristics ◽  
Self Adaptation ◽  
Nature Inspired Algorithms ◽  
Extensive Family ◽  
Self Adaptive

Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

scholarly journals Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika

2018 ◽  
Vol 8 (1) ◽  
pp. 99
Author(s):  
A. Y. Erwin Dodu ◽  
Deny Wiria Nugraha ◽  
Subkhan Dinda Putra
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Public Hospital ◽  
Memetic Algorithm ◽  
Optimal Schedule ◽  
Scheduling System ◽  
Local Search Methods ◽  
Evolution Algorithm ◽  
Central Sulawesi

The problem of midwife scheduling is one of the most frequent problems in hospitals. Midwife should be available 24 hours a day for a full week to meet the needs of the patient. Therefore, good or bad midwife scheduling result will have an impact on the quality of care on the patient and the health of the midwife on duty. The midwife scheduling process requires a lot of time, effort and good cooperation between some parties to solve this problem that is often faced by the Regional Public Hospital Undata Palu Central Sulawesi Province. This research aimed to apply Memetics algorithm to make scheduling system of midwifery staff at Regional Public Hospital Undata Palu Central Sulawesi Province that can facilitate the process of midwifery scheduling as well as to produce optimal schedule. The scheduling system created will follow the rules and policies applicable in the hospital and will also pay attention to the midwife's preferences on how to schedule them according to their habits and needs. Memetics algorithm is an optimization algorithm that combines Evolution Algorithm  and Local Search method. Evolution Algorithm in Memetics Algorithm generally refers to Genetic Algorithm so that the characteristics of Memetics Algotihm are identical with  Genetic Algorithm characteristics with the addition of Local Search methods. Local Search in Memetic Algorithm aims to improve the quality of an individual so it is expected to accelerate the time to get a solution.

An Interactive Visualization of Genetic Algorithm on 2-D Graph

2012 ◽  
Vol 4 (1) ◽  
pp. 34-54 ◽  
Author(s):  
Humera Farooq ◽  
Nordin Zakaria ◽  
Muhammad Tariq Siddique
Keyword(s):  
Genetic Algorithm ◽  
Growth Process ◽  
Search Space ◽  
Human Intervention ◽  
Branching Structures ◽  
Multidimensional Search ◽  
L System ◽  
User Fatigue ◽  
Search Data ◽  
Selection Of

The visualization of search space makes it easy to understand the behavior of the Genetic Algorithm (GA). The authors propose a novel way for representation of multidimensional search space of the GA using 2-D graph. This is carried out based on the gene values of the current generation, and human intervention is only required after several generations. The main contribution of this research is to propose an approach to visualize the GA search data and improve the searching process of the GA with human’s intention in different generations. Besides the selection of best individual or parents for the next generation, interference of human is required to propose a new individual in the search space. Active human intervention leads to a faster searching, resulting in less user fatigue. The experiments were carried out by evolving the parameters to derive the rules for a Parametric L-System. These rules are then used to model the growth process of branching structures in 3-D space. The experiments were conducted to evaluate the ability of the proposed approach to converge to optimized solution as compared to the Simple Genetic Algorithm (SGA).

Solving Multi-Objective Multicast Routing Problem Using a New Hybrid Approach

2018 ◽  
Vol 9 (4) ◽  
pp. 22-36
Author(s):  
Mohammed Mahseur ◽  
Abdelmadjid Boukra ◽  
Yassine Meraihi
Keyword(s):  
Multicast Routing ◽  
Hybrid Approach ◽  
Bat Algorithm ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Quality Of Service Parameters

Multicast routing is the problem of finding the spanning tree of a set of destinations whose roots are the source node and its leaves are the set of destination nodes by optimizing a set of quality of service parameters and satisfying a set of transmission constraints. This article proposes a new hybrid multicast algorithm called Hybrid Multi-objective Multicast Algorithm (HMMA) based on the Strength Pareto Evolutionary Algorithm (SPEA) to evaluate and classify the population in dominated solutions and non-dominated solutions. Dominated solutions are evolved by the Bat Algorithm, and non-dominated solutions are evolved by the Firefly Algorithm. Old and weak solutions are replaced by new random solutions by a process of mutation. The simulation results demonstrate that the proposed algorithm is able to find good Pareto optimal solutions compared to other algorithms.

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