Modified-Improved Fitness Dependent Optimizer for Complex and Engineering Problems
- URL: http://arxiv.org/abs/2407.14271v1
- Date: Thu, 27 Jun 2024 07:47:23 GMT
- Title: Modified-Improved Fitness Dependent Optimizer for Complex and Engineering Problems
- Authors: Hozan K. Hamarashid, Bryar A. Hassan, Tarik A. Rashid,
- Abstract summary: Fitness dependent (FDO) is considered one of the novel swarm intelligent algorithms.
This study proposes a modified version of IFDO, called M-IFDO.
M-IFDO is compared against five state-of-the-art algorithms.
- Score: 5.078139820108554
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Fitness dependent optimizer (FDO) is considered one of the novel swarm intelligent algorithms. Recently, FDO has been enhanced several times to improve its capability. One of the improvements is called improved FDO (IFDO). However, according to the research findings, the variants of FDO are constrained by two primary limitations that have been identified. Firstly, if the number of agents employed falls below five, it significantly diminishes the algorithm's precision. Secondly, the efficacy of FDO is intricately tied to the quantity of search agents utilized. To overcome these limitations, this study proposes a modified version of IFDO, called M-IFDO. The enhancement is conducted by updating the location of the scout bee to the IFDO to move the scout bees to achieve better performance and optimal solutions. More specifically, two parameters in IFDO, which are alignment and cohesion, are removed. Instead, the Lambda parameter is replaced in the place of alignment and cohesion. To verify the performance of the newly introduced algorithm, M-IFDO is tested on 19 basic benchmark functions, 10 IEEE Congress of Evolutionary Computation (CEC-C06 2019), and five real-world problems. M-IFDO is compared against five state-of-the-art algorithms: Improved Fitness Dependent Optimizer (IFDO), Improving Multi-Objective Differential Evolution algorithm (IMODE), Hybrid Sampling Evolution Strategy (HSES), Linear Success-History based Parameter Adaptation for Differential Evolution (LSHADE) and CMA-ES Integrated with an Occasional Restart Strategy and Increasing Population Size and An Iterative Local Search (NBIPOP-aCMAES). The verification criteria are based on how well the algorithm reaches convergence, memory usage, and statistical results. The results show that M-IFDO surpasses its competitors in several cases on the benchmark functions and five real-world problems.
Related papers
- A Fresh Look at Generalized Category Discovery through Non-negative Matrix Factorization [83.12938977698988]
Generalized Category Discovery (GCD) aims to classify both base and novel images using labeled base data.
Current approaches inadequately address the intrinsic optimization of the co-occurrence matrix $barA$ based on cosine similarity.
We propose a Non-Negative Generalized Category Discovery (NN-GCD) framework to address these deficiencies.
arXiv Detail & Related papers (2024-10-29T07:24:11Z) - Faster WIND: Accelerating Iterative Best-of-$N$ Distillation for LLM Alignment [81.84950252537618]
This paper reveals a unified game-theoretic connection between iterative BOND and self-play alignment.
We establish a novel framework, WIN rate Dominance (WIND), with a series of efficient algorithms for regularized win rate dominance optimization.
arXiv Detail & Related papers (2024-10-28T04:47:39Z) - LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning [56.273799410256075]
The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path.
The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability.
arXiv Detail & Related papers (2024-10-03T18:12:29Z) - Multi objective Fitness Dependent Optimizer Algorithm [19.535715565093764]
This paper proposes the multi objective variant of the recently introduced fitness dependent (FDO)
The algorithm is called a Multi objective Fitness Dependent (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and historical knowledge) as in FDO.
It is observed that the proposed algorithm successfully provides a wide variety of well-distributed feasible solutions, which enable the decision-makers to have more applicable-comfort choices to consider.
arXiv Detail & Related papers (2023-01-26T06:33:53Z) - Fitness Dependent Optimizer for IoT Healthcare using Adapted Parameters:
A Case Study Implementation [0.629786844297945]
This chapter discusses a case study on Fitness Dependentwarm or so-called FDO and adapting its parameters to the Internet of Things (IoT) healthcare.
Other algorithms are evaluated and compared to FDO as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the original work.
The target of this chapter's enhancement is to adapt the IoT healthcare framework based on FDO to spawn effective IoT healthcare applications.
arXiv Detail & Related papers (2022-05-18T16:18:57Z) - Effective Mutation Rate Adaptation through Group Elite Selection [50.88204196504888]
This paper introduces the Group Elite Selection of Mutation Rates (GESMR) algorithm.
GESMR co-evolves a population of solutions and a population of MRs, such that each MR is assigned to a group of solutions.
With the same number of function evaluations and with almost no overhead, GESMR converges faster and to better solutions than previous approaches.
arXiv Detail & Related papers (2022-04-11T01:08:26Z) - A Simple Evolutionary Algorithm for Multi-modal Multi-objective
Optimization [0.0]
We introduce a steady-state evolutionary algorithm for solving multi-modal, multi-objective optimization problems (MMOPs)
We report its performance on 21 MMOPs from various test suites that are widely used for benchmarking using a low computational budget of 1000 function evaluations.
arXiv Detail & Related papers (2022-01-18T03:31:11Z) - Result Diversification by Multi-objective Evolutionary Algorithms with
Theoretical Guarantees [94.72461292387146]
We propose to reformulate the result diversification problem as a bi-objective search problem, and solve it by a multi-objective evolutionary algorithm (EA)
We theoretically prove that the GSEMO can achieve the optimal-time approximation ratio, $1/2$.
When the objective function changes dynamically, the GSEMO can maintain this approximation ratio in running time, addressing the open question proposed by Borodin et al.
arXiv Detail & Related papers (2021-10-18T14:00:22Z) - Chaotic Fitness Dependent Optimizer for Planning and Engineering Design [1.1802674324027231]
Fitness Dependent (FDO) is a metaheuristic algorithm that mimics the reproduction behavior of the bee swarm in finding better hives.
This paper aims at improving the performance of FDO, thus, the chaotic theory is used inside FDO to propose Chaotic FDO (CFDO)
Ten chaotic maps are used in the CFDO to consider which of them are performing well to avoid local optima and finding global optima.
arXiv Detail & Related papers (2021-08-21T12:14:02Z) - Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices.
We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT)
Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
arXiv Detail & Related papers (2020-10-01T18:14:11Z) - Improved Fitness-Dependent Optimizer Algorithm [0.9990687944474739]
The fitness-dependent (FDO) algorithm was recently introduced in 2019.
An improved FDO algorithm is presented in this work.
To prove the practicability of the IFDO, it is used in real-world applications.
arXiv Detail & Related papers (2020-01-16T21:50:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.