Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Optimization Problems

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Application of Nonlinear Inequalities

Application of Nonlinear Inequalities

A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality: can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the key values are 3...

Heuristics

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...

Statically Indeterminate Problem Solving

Statically Indeterminate Problem Solving

Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Dose‑dependent association of smoking exposure with HDL subfraction cholesterol content.

Scientific reports·2026

Same author

Feeding Low- and High-Fibre Sunflower Meal to Broiler Chickens-Effects of Inclusion Rate and Age of Birds on the Production Traits, Carcass Composition, Nutrient Digestibility, Gut Viscosity, and Caecal Short-Chain Fatty Acid Content.

Animals : an open access journal from MDPI·2026

Same author

Assessment of health risks from exposure to indoor volatile organic compounds in European educational buildings.

Scientific reports·2026

Same author

Effects of litter exposure and flock age of broiler breeders on hatchability and the microbial composition of eggshells, egg membranes, and egg contents.

Frontiers in veterinary science·2025

Same author

Research note: Complex evaluation of whole oats and dehulled oats as feedstuffs for broiler chickens.

Poultry science·2025

Same author

Even Low Amounts of Amorphous Lignocellulose Affect Some Upper Gut Parameters, but They Do Not Modify Ileal Microbiota in Young Broiler Chickens.

Animals : an open access journal from MDPI·2025

Search research articles

Related Experiment Videos

A comparison of global search algorithms for continuous black box optimization.

Petr Pošík¹, Waltraud Huyer, László Pál

¹Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic. posik@labe.felk.cvut.cz

Evolutionary Computation

|June 20, 2012

Summary

This study compares four mathematical programming optimization methods against an evolutionary algorithm. Results guide algorithm selection based on function evaluations and suggest hybridizing evolutionary algorithms with other methods.

Related Experiment Videos

Area of Science:

Numerical Optimization
Mathematical Programming
Evolutionary Computation

Background:

Black box optimization problems are common in science and engineering.
Evolutionary algorithms (EAs) are popular for these problems, but other methods exist.
Comparing diverse optimization strategies is crucial for advancing the field.

Purpose of the Study:

To experimentally compare four mathematical programming-based global numerical black box optimization methods.
To evaluate these methods against the state-of-the-art evolutionary algorithm, BIPOP-CMA-ES.
To provide guidance on algorithm selection based on computational budget.

Main Methods:

The study employed the Comparing Continuous Optimizers (COCO) methodology for a standardized comparison.
Four methods were selected: DIRECT, MCS (with local search), NEWUOA, and GLOBAL (multi-start).
Performance was evaluated against BIPOP-CMA-ES across various benchmark problems.

Main Results:

The comparison revealed performance differences based on the budget of function evaluations.
DIRECT and MCS showed strengths in systematic sampling, while NEWUOA and GLOBAL utilized multi-start local search.
No single algorithm universally outperformed others across all scenarios.

Conclusions:

Algorithm choice depends on the available function evaluations and problem characteristics.
Hybridization of EAs with features from mathematical programming methods is a promising research direction.
Further research can explore tailored hybrid strategies for specific optimization challenges.