Table 1 The comparative analysis of algorithms.

ACO

Randomly searches for food

sources (pheromone-based)

Refines paths based on

pheromone intensity

Effective for routing problems,

easy to implement

Tends to get trapped in local

optima, slow convergence

N/A

PSO

Swarm-based global search

using particle positions

Updates particles based

on local best

Widely used in continuous

optimization, fast convergence

Premature convergence

in complex spaces

N/A

GWO

Mimics wolf pack hunting, balancing

exploration and exploitation

Focuses on hunting the

best prey (solution)

Effective in continuous

optimization

Can suffer from stagnation

in complex search spaces

N/A

CS

Random walk inspired by cuckoo

birds’ parasitism behavior

Elite solutions drive

reproduction

Good for multimodal problems,

simple to implement

Poor global search in high-

dimensional spaces

N/A

NMRA

Random exploration by workers,

mating behavior of mole-rats

Focuses on exploitation

using best solutions

Effective for low-dimensional

optimization

Poor exploration and stagnation

in high-dimensional spaces

Improved exploration with

multi-algorithm strategy

SSA

Simulates salps’ movement

in the ocean

Focuses on optimizing positions

based on best salps

Strong exploration, especially

in continuous problems

Susceptible to stagnation

in complex spaces

N/A

SOA

Inspired by seagulls’

migratory behavior

Uses the best seagull for

optimal exploitation

Effective in solving complex

and large-scale problems

May face issues with fine-tuning

for specific problems

N/A

SSNMRA (Proposed)

Integrates SSA, SOA and NMRA

strategies in iterative phases

Retains NMRA’s exploitation

phase, adds stagnation check

Strong exploration and exploitation,

self-adaptive

Still under evaluation in

real-world applications

Multi-algorithm strategy with stagnation

phase, self-adaptive mutation