Dear Nabajyoti Bhattacharje,

I suggest you to see links and attached files on topic.

https://ieeexplore.ieee.org/document/7105570

https://hal.archives-ouvertes.fr/hal-02571791/document

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122827

Conference Paper Applying particle swarm optimization to software testing

Article Performance evaluation of the particle swarm optimization al...

Best regards

Hello dear Nabajyoti Bhattacharjee

Performance analysis of a swarm algorithm is a necessary task to perform and must be done on a fair basis. A theoretical approach is generally not sufficient to evaluate a swarm algorithm. To addresses some guidelines of evaluating experimentally a swarm algorithm and/or comparing metaheuristics in a rigorous way. To evaluate the performance of a swarm algorithm in a rigorous manner, the following three steps must be considered:

1) Experimental design: In the first step, the goals of the experiments, the selected

instances, and factors have to be defined.

2) Measurement: In the second step, the measures to compute are selected. After

executing the different experiments, statistical analysis is applied to the obtained

results. The performance analysis must be done with state-of-the-art optimization

algorithms dedicated to the problem.

3) Reporting: Finally, the results are presented in a comprehensive way, and an

analysis is carried out following the defined goals. Another main issue here is to

ensure the reproducibility of the computational experiments.

T. Bartz-Beielstein. Experimental Research in Evolutionary Computation. Springer, 2006.

I hope; I made sense.

Solving the test problems like Ackley, Rosenbrock, Sphere etc to evaluate the effectiveness and speed of algorithm and on comparing the results with the established results of different standard algorithms may be one of the way to test the efficiency of developed algorithm

Indeed, it's based on the problem space that you need to solve whether (Continuous or Combinatorial/discrete). All these Functions have mentioned like Ackley, Rosenbrock, Sphere it's continuous problem.

Subsequently, if you proposed a new algorithm or you have a modification in any algorithm, of course, you need to use many of these Function problems to test the efficiency of the developed algorithm.

In case your problem based on combinatorial/discrete you need to use the benchmark datasets of the problem like vehicle routing problem, travel seaman problem, time tableting problem, and so on to test the efficiency of the developed algorithm.

Regards

Thank you sir for the nice explanation

Dear Nabajyoti Bhattacharjee

Kindly check the attached links and files.

Article A Comprehensive Review of Swarm Optimization Algorithms

https://ieeexplore.ieee.org/abstract/document/6426820

https://ieeexplore.ieee.org/abstract/document/6726474

Article Applications of Particle Swarm Optimization in Geotechnical ...

The convergence rate and statistical tests results are very popular in order to evaluate and compare the performance of swarm or evolutionary algorithm. In the following two relative works are

Article New Insights into Position optimisation of Wave Energy Conve...

Article A Hybrid Cooperative Co-evolution Algorithm Framework for Op...