scholarly journals Shrimp Feed Formulation via Evolutionary Algorithm with Power Heuristics for Handling Constraints

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Rosshairy Abd. Rahman ◽  
Graham Kendall ◽  
Razamin Ramli ◽  
Zainoddin Jamari ◽  
Ku Ruhana Ku-Mahamud
Keyword(s):  
Evolutionary Algorithm ◽  
Early Stage ◽  
Feed Formulation ◽  
Search Mechanism ◽  
Diet Formulation ◽  
Shrimp Feed ◽  
Nutrition Value ◽  
Handling Technique ◽  
Necessary Constraints ◽  
Initialization Procedure

Formulating feed for shrimps represents a challenge to farmers and industry partners. Most previous studies selected from only a small number of ingredients due to cost pressures, even though hundreds of potential ingredients could be used in the shrimp feed mix. Even with a limited number of ingredients, the best combination of the most appropriate ingredients is still difficult to obtain due to various constraint requirements, such as nutrition value and cost. This paper proposes a new operator which we call Power Heuristics, as part of an Evolutionary Algorithm (EA), which acts as a constraint handling technique for the shrimp feed or diet formulation. The operator is able to choose and discard certain ingredients by utilising a specialized search mechanism. The aim is to achieve the most appropriate combination of ingredients. Power Heuristics are embedded in the EA at the early stage of a semirandom initialization procedure. The resulting combination of ingredients, after fulfilling all the necessary constraints, shows that this operator is useful in discarding inappropriate ingredients when a crucial constraint is violated.

Prophase Failure Prediction of the Mechanical Transmission Systems Based on the Biological Evolutionary Algorithm

2013 ◽  
Vol 572 ◽  
pp. 447-450
Author(s):  
Xiao Hui Chen ◽  
Lei Xiao ◽  
Zhen Xiang Liu ◽  
Chuang Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Early Stage ◽  
Gear Tooth ◽  
Fault Prediction ◽  
Learning Ability ◽  
Support Vector ◽  
Mechanical Transmission ◽  
Test Bed ◽  
Transmission Systems ◽  
Artificial Intelligence Algorithm

There are several types of the mechanical transmission failure, such as gear tooth broken, fatigue, pitting etc. The deterioration pattern of each failure varies according to the different environment. Furthermore, setting up the fault prediction model is quite difficult, especially at the early stage of the fault. In order to predict the prophase failure of the mechanical transmission systems depends on the condition monitoring signal, this paper researched on the biological evolutionary algorithm combined with other artificial intelligence algorithm. As the case study, the typical failure of the gearbox test-bed, for example gear tooth broken or fatigue at the test-bed was monitored by several sensors. An improved support vector machine (SVM) optimized by genetic algorithm (GA) was chosen to predict the prophase failure of the gear, due to its self-adaption and self-learning ability. The prediction results showed that it simulated the failure pattern well on the condition of a few sample data.

scholarly journals A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Leilei Cao ◽  
Lihong Xu ◽  
Erik D. Goodman
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Its Region ◽  
Bat Algorithm ◽  
Greedy Strategy ◽  
Search Mechanism ◽  
Genotype Space

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

scholarly journals Evolutionary Algorithm with Roulette-Tournament Selection for Solving Aquaculture Diet Formulation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Rosshairy Abd Rahman ◽  
Razamin Ramli ◽  
Zainoddin Jamari ◽  
Ku Ruhana Ku-Mahamud
Keyword(s):  
Evolutionary Algorithm ◽  
The Other ◽  
Roulette Wheel ◽  
Diet Formulation ◽  
Tournament Selection ◽  
Selection Operators ◽  
Selection For ◽  
Feasible Solutions ◽  
Selection Operator ◽  
Formulation Problem

The function of operators in an evolutionary algorithm (EA) is very crucial as the operators have a strong effect on the performance of the EA. In this paper, a new selection operator is introduced for a real valued encoding problem, which specifically exists in a shrimp diet formulation problem. This newly developed selection operator is a hybrid between two well-known established selection operators: roulette wheel and binary tournament selection. A comparison of the performance of the proposed operator and the other existing operator was made for evaluation purposes. The result shows that the proposed roulette-tournament selection is better in terms of its ability to provide many good feasible solutions when a population size of 30 is used. Thus, the proposed roulette-tournament is suitable and comparable to established selection for solving a real valued shrimp diet formulation problem. The selection operator can also be generalized to any problems related to EA.

scholarly journals Eco-Friendly Feed Formulation and On-Farm Feed Production as Ways to Reduce the Environmental Impacts of Pig Production Without Consequences on Animal Performance

2021 ◽  
Vol 8 ◽  
Author(s):  
Francine de Quelen ◽  
Ludovic Brossard ◽  
Aurélie Wilfart ◽  
Jean-Yves Dourmad ◽  
Florence Garcia-Launay
Keyword(s):  
Environmental Impacts ◽  
General Pattern ◽  
Dietary Treatment ◽  
Animal Performance ◽  
Land Occupation ◽  
Feed Ingredients ◽  
Pig Production ◽  
Feed Formulation ◽  
Diet Formulation ◽  
The Impact

Animal feeding has a major contribution to the environmental impacts of pig production. One potential way to mitigate such effects is to incorporate an assessment of these impacts in the feed formulation process. The objective of this study was to test the ability of innovative formulation methodologies to reduce the impacts of pig production while also taking into account possible effects on growth performance. We compared three different formulation methodologies: least-cost formulation, in accordance with standard practices on commercial farms; multiobjective (MO) formulation, which considered feed cost and environmental impacts as calculated by life cycle assessment (LCA); and MO formulation, which prioritized locally produced feed ingredients to reduce the impact of transport. Ninety-six pigs were distributed between three experimental groups, with pigs individually weighted and fed using an automatic feeding system from 40 to 115 kg body weight. Based on the experimental results, six categories of impacts were evaluated: climate change (CC), demand in non-renewable energy (NRE), acidification (AC), eutrophication (EU), land occupation (LO), and phosphorus demand (PD), at both feed plant gate and farm gate, with 1 kg of feed and 1 kg of live pig as functional units, respectively. At feed level, MO formulations reduced CC, NRE, AC, and PD impacts but sometimes increased LO and EU impacts. These formulations reduced the proportion of cereals and oil meals into feeds (feed ingredients with high impacts), while the proportion of alternative protein sources, like peas, faba beans, or high-protein agricultural coproducts increased (feed ingredients with low impacts). Overall, animal performance was not affected by the dietary treatment; because of this, the general pattern of results obtained with either MO formulation at farm gate was similar to that obtained at feed level. Thus, MO diet formulation represents an efficient way to reduce the environmental impacts of pig production without compromising animal performance.

scholarly journals An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yufang Qin ◽  
Junzhong Ji ◽  
Chunnian Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Optimization Problem ◽  
State Of The Art ◽  
Early Stage ◽  
Test Problems ◽  
Multiobjective Optimization Problem ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the population to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to maintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely used test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and diversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE.

scholarly journals Balancing Exploration and Exploitation With Decomposition-Based Dynamic Multi-Objective Evolutionary Algorithm

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Qing Zhang ◽  
Ruwang Jiao ◽  
Sanyou Zeng ◽  
Zhigao Zeng
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Early Stage ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Exploration And Exploitation ◽  
Multi Objective ◽  
Count Function

Balancing exploration and exploitation is a crucial issue in evolutionary global optimization. This paper proposes a decomposition-based dynamic multi-objective evolutionary algorithm for addressing global optimization problems. In the proposed method, the niche count function is regarded as a helper objective to maintain the population diversity, which converts a global optimization problem to a multi-objective optimization problem (MOP). The niche-count value is controlled by the niche radius. In the early stage of evolution, a large niche radius promotes the population diversity for better exploration; in the later stage of evolution, a niche radius close to 0 focuses on convergence for better exploitation. Through the whole evolution process, the niche radius is dynamically decreased from a large value to zero, which can provide a sound balance between the exploration and exploitation. Experimental results on CEC 2014 benchmark problems reveal that the proposed algorithm is capable of offering high-quality solutions, in comparison with four state-of-the-art algorithms.

FSB-EA: Fuzzy search bias guided constraint handling technique for evolutionary algorithm

2019 ◽  
Vol 119 ◽  
pp. 20-35
Author(s):  
Zhiyong Li ◽  
Shaomiao Chen ◽  
Shiwen Zhang ◽  
Shilong Jiang ◽  
Yu Gu ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Constraint Handling ◽  
Fuzzy Search ◽  
Handling Technique ◽  
Search Bias
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