Metaheuristic Optimization Specialist Needed
Budget: $10 – $30 USD
I am seeking an experienced specialist with knowledge in metaheuristics optimization problems. This project involves algorithmic problem-solving, in particular, addressing aspects of the Knapsack and Bin Packing Problems.
Project Requirements:
1. Consideration for Limited Capacity: Prepare solutions taking into account a predetermined capacity.
2. Weight & Dimension Constraints: Present solutions accommodating for specified item weights and dimensions.
3. Preferred Algorithm Usage: Apply Simulated Annealing, Hill-Climbing, Steepest Ascent Hill-Climbing, Steepest Ascent Hill-Climbing with Replacement, Hill-Climbing with Random Restarts and Tabu Search algorithms for problem-solving in accordance with the project parameters.
4. create a detailed report on the analysis of:
a. Explain representation
b. Explain tweaking operators and stopping criteria.
c. Implement corresponding solutions.
d. For each instance for each algorithm, repeat the runs 10 times, plot the corresponding average, and
the confidence interval of the “best-so-far” and the “current best” across iterations.
e. Report the descriptive statics of the final results along with the Confidence Intervals plots and Box
Plots.
3. Comment on the results. Include the results of FF, BF, FFD, and BFD in your discussion
Ideal candidates should be proficient in the application and understanding of these chosen algorithms. Prior experience with similar optimization problems is a bonus. Looking forward to working with someone who can deliver efficient, optimal solutions amidst stringent constraints.
Project Requirements:
1. Consideration for Limited Capacity: Prepare solutions taking into account a predetermined capacity.
2. Weight & Dimension Constraints: Present solutions accommodating for specified item weights and dimensions.
3. Preferred Algorithm Usage: Apply Simulated Annealing, Hill-Climbing, Steepest Ascent Hill-Climbing, Steepest Ascent Hill-Climbing with Replacement, Hill-Climbing with Random Restarts and Tabu Search algorithms for problem-solving in accordance with the project parameters.
4. create a detailed report on the analysis of:
a. Explain representation
b. Explain tweaking operators and stopping criteria.
c. Implement corresponding solutions.
d. For each instance for each algorithm, repeat the runs 10 times, plot the corresponding average, and
the confidence interval of the “best-so-far” and the “current best” across iterations.
e. Report the descriptive statics of the final results along with the Confidence Intervals plots and Box
Plots.
3. Comment on the results. Include the results of FF, BF, FFD, and BFD in your discussion
Ideal candidates should be proficient in the application and understanding of these chosen algorithms. Prior experience with similar optimization problems is a bonus. Looking forward to working with someone who can deliver efficient, optimal solutions amidst stringent constraints.