A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems

<table class="algorithm-group"><tr><td><table class="algorithm" id="alg2"><tr><td> </td><td>Decode <svg height="11.927pt" id="M187" style="vertical-align:-3.291101pt" version="1.1" viewbox="-0.0498162 -8.6359 13.0724 11.927" width="13.0724pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M748 650H522L515 622L546 617C580 611 587 604 565 575C518 513 469 451 419 393C376 474 349 534 330 580C318 609 325 612 361 618L383 622L392 650H151L144 622C214 616 224 612 257 543L360 327C270 218 187 124 159 95C106 40 92 34 26 28L17 0H252L259 28L236 31C189 37 188 47 209 78C249 136 308 210 377 294L478 79C494 44 487 37 449 32L418 28L409 0H673L680 28C596 34 591 39 554 116L436 361C526 469 574 521 604 553C659 612 669 614 739 622L748 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,9.88,3.132)"><path d="M250 606C250 634 233 656 203 656C168 656 146 618 146 593C146 564 169 545 192 545C227 545 250 573 250 606ZM227 95L212 119C187 98 152 71 135 71C129 71 128 78 134 102L207 373C219 418 217 451 194 451C165 451 92 411 30 351L44 326C77 353 106 371 114 371C124 371 121 357 117 341L55 97C32 5 46 -12 70 -12C108 -12 191 51 227 95Z"></path></g></svg> to <svg height="6.1673pt" id="M188" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -5.96091 9.39034 6.1673" width="9.39034pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M689 332C689 394 670 448 646 448C620 448 597 421 597 396C597 386 600 381 608 372C619 359 620 334 620 315C620 150 538 45 454 45C414 45 386 67 386 122C386 138 388 158 394 180L457 426L452 432L377 416L315 156C302 100 259 45 216 45C176 45 148 67 148 122C148 133 152 158 156 180C162 212 173 259 194 332C201 357 206 384 206 405C206 430 198 448 174 448C125 448 66 406 23 342L43 319C84 368 110 383 121 383C126 383 128 382 128 377C128 370 127 359 122 343C99 268 84 204 77 156C74 137 70 111 70 104C70 25 125 -12 180 -12C228 -12 276 12 319 50C338 8 378 -12 418 -12C549 -12 689 166 689 332Z"></path></g></svg> cluster centers following equation (<a href="https://static.hindawi.com/articles/cin/volume-2020/5787642/figures/#EEq12">12</a>)</td></tr><tr><td> </td><td>Calculate Euclidean distance between all data objects and clustering centers following equation (<a href="https://static.hindawi.com/articles/cin/volume-2020/5787642/figures/#EEq9">10</a>)</td></tr><tr><td> </td><td>Distribute the data to the nearest clustering center</td></tr><tr><td> </td><td>Calculate the total within-cluster variance following equation (<a href="https://static.hindawi.com/articles/cin/volume-2020/5787642/figures/#EEq9">9</a>)</td></tr><tr><td> </td><td>Return fitness</td></tr></table></td></tr></table>

<div> Pseudocode of fitness calculation.</div>

Computational Intelligence and Neuroscience

alg2

Algorithm 2

Algorithm 2: A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems 