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Figure 1. An Example of local and global search D. An Illustrative Example

Figure 1 An Example of local and global search D. An Illustrative Example

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In the algorithm, the indigence condition Avg(i) + Taye/c < Tayg 1s used to estimate the quality of a colony. If the fitness is high or in an developing in the acceptable range, then the colony keeps ocal area; otherwise it must be deleted and MRO globally searches for a new location in the problem space by distributing spores using the movement equation. Here the threshold parameter c helps the algorithm to decide about the colonies that should be destroyed w. r. t. their qualities. The second condition F(X;) < F(X;) recognizes better colonies to areas. By conside MRO algorithm s be used by the algorithm as clues of richer ring the locations of quality colonies, the uccessively searches the problem space to find better locations. The global best solution and its fitness value are the outputs of MRO algorithm.
In the algorithm, the indigence condition Avg(i) + Taye/c < Tayg 1s used to estimate the quality of a colony. If the fitness is high or in an developing in the acceptable range, then the colony keeps ocal area; otherwise it must be deleted and MRO globally searches for a new location in the problem space by distributing spores using the movement equation. Here the threshold parameter c helps the algorithm to decide about the colonies that should be destroyed w. r. t. their qualities. The second condition F(X;) < F(X;) recognizes better colonies to areas. By conside MRO algorithm s be used by the algorithm as clues of richer ring the locations of quality colonies, the uccessively searches the problem space to find better locations. The global best solution and its fitness value are the outputs of MRO algorithm.
Figure 3. The 3-D plot of the peak function (left) and the contour map of the peak function (right) Figure 2. Mushroom Reproduction Optimization Algorithm
Figure 3. The 3-D plot of the peak function (left) and the contour map of the peak function (right) Figure 2. Mushroom Reproduction Optimization Algorithm
Figure 4. Running steps of MRO to find the global best of the peak function
Figure 4. Running steps of MRO to find the global best of the peak function
STATISTICAL RESULTS OF MRO AND NATURE-INSPIRED ALGORITHMS ON HIGH DIMENSION FUNCTIONS Table III mance over FA, KH, DE, GSA, COA and ISA. There is no significant performance between MRO, PSO, APSO and BBO. in the neighbourhood of a parent, it will be found with a high probability.
STATISTICAL RESULTS OF MRO AND NATURE-INSPIRED ALGORITHMS ON HIGH DIMENSION FUNCTIONS Table III mance over FA, KH, DE, GSA, COA and ISA. There is no significant performance between MRO, PSO, APSO and BBO. in the neighbourhood of a parent, it will be found with a high probability.
HIGH AND LOW DIMENSIONAL BENCHMARK FUNCTIONS Table II
HIGH AND LOW DIMENSIONAL BENCHMARK FUNCTIONS Table II
STATISTICAL RESULTS OF MRO AND NATURE-INSPIRED ALGORITHMS ON LOW DIMENSION FUNCTIONS Table V Table IV
STATISTICAL RESULTS OF MRO AND NATURE-INSPIRED ALGORITHMS ON LOW DIMENSION FUNCTIONS Table V Table IV
PERFORMANCE COMPARISON OF MRO AND NATURE INSPIRED ALGORITHMS ON BENCHMARK FUNCTIONS
PERFORMANCE COMPARISON OF MRO AND NATURE INSPIRED ALGORITHMS ON BENCHMARK FUNCTIONS
STATISTICAL COMPARISON OF MRO AND OTHER META-HEURISTIC ALGORITHMS beam) and x4 (thickness of the beam). The objective function to be minimized is given below:
STATISTICAL COMPARISON OF MRO AND OTHER META-HEURISTIC ALGORITHMS beam) and x4 (thickness of the beam). The objective function to be minimized is given below:
Figure 5. Schematic of the welded beam problem [21]
Figure 5. Schematic of the welded beam problem [21]
STATISTICAL RESULTS OF THE WELDED BEAM DESIGN PROBLEM OBTAINED BY MRO AND NATURE-INSPIRED ALGORITHMS Table VIII
STATISTICAL RESULTS OF THE WELDED BEAM DESIGN PROBLEM OBTAINED BY MRO AND NATURE-INSPIRED ALGORITHMS Table VIII
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Evolutionary algorithmsOperations research and Optimization
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